Overview

Dataset statistics

Number of variables50
Number of observations2845342
Missing cells3414349
Missing cells (%)2.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory838.5 MiB
Average record size in memory309.0 B

Variable types

Categorical21
DateTime2
Numeric13
Boolean13
Unsupported1

Alerts

Country has constant value "US"Constant
Turning_Loop has constant value "False"Constant
ID has a high cardinality: 2845342 distinct valuesHigh cardinality
Description has a high cardinality: 1174563 distinct valuesHigh cardinality
Street has a high cardinality: 159651 distinct valuesHigh cardinality
City has a high cardinality: 11681 distinct valuesHigh cardinality
County has a high cardinality: 1707 distinct valuesHigh cardinality
Zipcode has a high cardinality: 363085 distinct valuesHigh cardinality
Airport_Code has a high cardinality: 2004 distinct valuesHigh cardinality
Weather_Timestamp has a high cardinality: 474214 distinct valuesHigh cardinality
Weather_Condition has a high cardinality: 127 distinct valuesHigh cardinality
Start_Lat is highly overall correlated with End_Lat and 1 other fieldsHigh correlation
Start_Lng is highly overall correlated with End_Lng and 2 other fieldsHigh correlation
End_Lat is highly overall correlated with Start_Lat and 1 other fieldsHigh correlation
End_Lng is highly overall correlated with Start_Lng and 2 other fieldsHigh correlation
Temperature(F) is highly overall correlated with Wind_Chill(F)High correlation
Wind_Chill(F) is highly overall correlated with Temperature(F)High correlation
State is highly overall correlated with Start_Lat and 4 other fieldsHigh correlation
Timezone is highly overall correlated with Start_Lng and 2 other fieldsHigh correlation
Bump is highly overall correlated with Traffic_CalmingHigh correlation
Traffic_Calming is highly overall correlated with BumpHigh correlation
Sunrise_Sunset is highly overall correlated with Civil_Twilight and 2 other fieldsHigh correlation
Civil_Twilight is highly overall correlated with Sunrise_Sunset and 2 other fieldsHigh correlation
Nautical_Twilight is highly overall correlated with Sunrise_Sunset and 2 other fieldsHigh correlation
Astronomical_Twilight is highly overall correlated with Sunrise_Sunset and 2 other fieldsHigh correlation
traffic_event_type is highly overall correlated with traffic_event_type_extHigh correlation
traffic_event_type_ext is highly overall correlated with traffic_event_typeHigh correlation
Severity is highly imbalanced (67.8%)Imbalance
Side is highly imbalanced (58.1%)Imbalance
Weather_Condition is highly imbalanced (56.4%)Imbalance
Amenity is highly imbalanced (92.0%)Imbalance
Bump is highly imbalanced (99.5%)Imbalance
Crossing is highly imbalanced (63.3%)Imbalance
Give_Way is highly imbalanced (97.6%)Imbalance
Junction is highly imbalanced (52.4%)Imbalance
No_Exit is highly imbalanced (98.4%)Imbalance
Railway is highly imbalanced (93.3%)Imbalance
Roundabout is highly imbalanced (99.9%)Imbalance
Station is highly imbalanced (83.7%)Imbalance
Stop is highly imbalanced (87.2%)Imbalance
Traffic_Calming is highly imbalanced (99.3%)Imbalance
Traffic_Signal is highly imbalanced (55.3%)Imbalance
Number has 1743911 (61.3%) missing valuesMissing
Weather_Timestamp has 50736 (1.8%) missing valuesMissing
Temperature(F) has 69274 (2.4%) missing valuesMissing
Wind_Chill(F) has 469643 (16.5%) missing valuesMissing
Humidity(%) has 73092 (2.6%) missing valuesMissing
Pressure(in) has 59200 (2.1%) missing valuesMissing
Visibility(mi) has 70546 (2.5%) missing valuesMissing
Wind_Direction has 73775 (2.6%) missing valuesMissing
Wind_Speed(mph) has 157944 (5.6%) missing valuesMissing
Precipitation(in) has 549458 (19.3%) missing valuesMissing
Weather_Condition has 70636 (2.5%) missing valuesMissing
Number is highly skewed (γ1 = 156.9450181)Skewed
Precipitation(in) is highly skewed (γ1 = 106.2589449)Skewed
ID is uniformly distributedUniform
ID has unique valuesUnique
date is an unsupported type, check if it needs cleaning or further analysisUnsupported
Distance(mi) has 385441 (13.5%) zerosZeros
Wind_Speed(mph) has 433636 (15.2%) zerosZeros
Precipitation(in) has 2104242 (74.0%) zerosZeros

Reproduction

Analysis started2023-03-01 00:02:17.228521
Analysis finished2023-03-01 00:09:26.103317
Duration7 minutes and 8.87 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

ID
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct2845342
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size21.7 MiB
A-1
 
1
A-1896898
 
1
A-1896890
 
1
A-1896891
 
1
A-1896892
 
1
Other values (2845337)
2845337 

Length

Max length9
Median length9
Mean length8.6095007
Min length3

Characters and Unicode

Total characters24496974
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2845342 ?
Unique (%)100.0%

Sample

1st rowA-1
2nd rowA-2
3rd rowA-3
4th rowA-4
5th rowA-5

Common Values

ValueCountFrequency (%)
A-1 1
 
< 0.1%
A-1896898 1
 
< 0.1%
A-1896890 1
 
< 0.1%
A-1896891 1
 
< 0.1%
A-1896892 1
 
< 0.1%
A-1896893 1
 
< 0.1%
A-1896894 1
 
< 0.1%
A-1896895 1
 
< 0.1%
A-1896896 1
 
< 0.1%
A-1896897 1
 
< 0.1%
Other values (2845332) 2845332
> 99.9%

Length

2023-02-28T17:09:26.219877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a-1 1
 
< 0.1%
a-6 1
 
< 0.1%
a-42 1
 
< 0.1%
a-24 1
 
< 0.1%
a-22 1
 
< 0.1%
a-86 1
 
< 0.1%
a-11 1
 
< 0.1%
a-3 1
 
< 0.1%
a-4 1
 
< 0.1%
a-5 1
 
< 0.1%
Other values (2845332) 2845332
> 99.9%

Most occurring characters

ValueCountFrequency (%)
A 2845342
11.6%
- 2845342
11.6%
1 2728675
11.1%
2 2574018
10.5%
3 1728617
7.1%
4 1723910
7.0%
5 1717907
7.0%
6 1717564
7.0%
7 1717564
7.0%
8 1662907
6.8%
Other values (2) 3235128
13.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 18806290
76.8%
Uppercase Letter 2845342
 
11.6%
Dash Punctuation 2845342
 
11.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2728675
14.5%
2 2574018
13.7%
3 1728617
9.2%
4 1723910
9.2%
5 1717907
9.1%
6 1717564
9.1%
7 1717564
9.1%
8 1662907
8.8%
9 1617564
8.6%
0 1617564
8.6%
Uppercase Letter
ValueCountFrequency (%)
A 2845342
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2845342
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21651632
88.4%
Latin 2845342
 
11.6%

Most frequent character per script

Common
ValueCountFrequency (%)
- 2845342
13.1%
1 2728675
12.6%
2 2574018
11.9%
3 1728617
8.0%
4 1723910
8.0%
5 1717907
7.9%
6 1717564
7.9%
7 1717564
7.9%
8 1662907
7.7%
9 1617564
7.5%
Latin
ValueCountFrequency (%)
A 2845342
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24496974
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 2845342
11.6%
- 2845342
11.6%
1 2728675
11.1%
2 2574018
10.5%
3 1728617
7.1%
4 1723910
7.0%
5 1717907
7.0%
6 1717564
7.0%
7 1717564
7.0%
8 1662907
6.8%
Other values (2) 3235128
13.2%

Severity
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.7 MiB
2
2532991 
3
 
155105
4
 
131193
1
 
26053

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2845342
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row2
4th row2
5th row3

Common Values

ValueCountFrequency (%)
2 2532991
89.0%
3 155105
 
5.5%
4 131193
 
4.6%
1 26053
 
0.9%

Length

2023-02-28T17:09:26.290776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T17:09:26.351477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2 2532991
89.0%
3 155105
 
5.5%
4 131193
 
4.6%
1 26053
 
0.9%

Most occurring characters

ValueCountFrequency (%)
2 2532991
89.0%
3 155105
 
5.5%
4 131193
 
4.6%
1 26053
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2845342
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 2532991
89.0%
3 155105
 
5.5%
4 131193
 
4.6%
1 26053
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 2845342
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 2532991
89.0%
3 155105
 
5.5%
4 131193
 
4.6%
1 26053
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2845342
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 2532991
89.0%
3 155105
 
5.5%
4 131193
 
4.6%
1 26053
 
0.9%
Distinct1807311
Distinct (%)63.5%
Missing0
Missing (%)0.0%
Memory size21.7 MiB
Minimum2016-01-14 20:18:33
Maximum2021-12-31 23:30:00
2023-02-28T17:09:26.416959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:09:26.497132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2239983
Distinct (%)78.7%
Missing0
Missing (%)0.0%
Memory size21.7 MiB
Minimum2016-02-08 06:37:08
Maximum2022-01-01 00:00:00
2023-02-28T17:09:26.579835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:09:26.662999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Start_Lat
Real number (ℝ)

Distinct1093618
Distinct (%)38.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.245201
Minimum24.566027
Maximum49.00058
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.7 MiB
2023-02-28T17:09:26.751201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum24.566027
5-th percentile25.961255
Q133.445174
median36.098609
Q340.160243
95-th percentile45.089827
Maximum49.00058
Range24.434553
Interquartile range (IQR)6.715069

Descriptive statistics

Standard deviation5.3637975
Coefficient of variation (CV)0.14798642
Kurtosis-0.58706206
Mean36.245201
Median Absolute Deviation (MAD)3.376716
Skewness-0.11463983
Sum1.0312999 × 108
Variance28.770323
MonotonicityNot monotonic
2023-02-28T17:09:26.826918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.702455 348
 
< 0.1%
27.447751 302
 
< 0.1%
28.452192 285
 
< 0.1%
28.449924 268
 
< 0.1%
40.85306 255
 
< 0.1%
25.689146 254
 
< 0.1%
25.712548 238
 
< 0.1%
34.020212 235
 
< 0.1%
28.452939 231
 
< 0.1%
25.810617 227
 
< 0.1%
Other values (1093608) 2842699
99.9%
ValueCountFrequency (%)
24.566027 1
 
< 0.1%
24.570087 1
 
< 0.1%
24.570222 1
 
< 0.1%
24.57033 1
 
< 0.1%
24.570584 1
 
< 0.1%
24.571202 3
< 0.1%
24.57124 1
 
< 0.1%
24.571308 1
 
< 0.1%
24.57131 1
 
< 0.1%
24.571536 1
 
< 0.1%
ValueCountFrequency (%)
49.00058 1
 
< 0.1%
49.00056 1
 
< 0.1%
49.000269 1
 
< 0.1%
49.00026 1
 
< 0.1%
48.99951 1
 
< 0.1%
48.998445 3
< 0.1%
48.99838 1
 
< 0.1%
48.997457 1
 
< 0.1%
48.996539 1
 
< 0.1%
48.996014 1
 
< 0.1%

Start_Lng
Real number (ℝ)

Distinct1120365
Distinct (%)39.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-97.114633
Minimum-124.54807
Maximum-67.113167
Zeros0
Zeros (%)0.0%
Negative2845342
Negative (%)100.0%
Memory size21.7 MiB
2023-02-28T17:09:26.907964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-124.54807
5-th percentile-122.32597
Q1-118.03311
median-92.418076
Q3-80.372431
95-th percentile-74.174027
Maximum-67.113167
Range57.434907
Interquartile range (IQR)37.660681

Descriptive statistics

Standard deviation18.317819
Coefficient of variation (CV)-0.18862059
Kurtosis-1.64946
Mean-97.114633
Median Absolute Deviation (MAD)15.437108
Skewness-0.2388874
Sum-2.7632434 × 108
Variance335.5425
MonotonicityNot monotonic
2023-02-28T17:09:26.979512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-80.332105 348
 
< 0.1%
-81.400388 285
 
< 0.1%
-81.479136 268
 
< 0.1%
-80.382872 254
 
< 0.1%
-73.96011 248
 
< 0.1%
-80.382427 238
 
< 0.1%
-117.814998 235
 
< 0.1%
-81.400159 231
 
< 0.1%
-77.478828 218
 
< 0.1%
-82.573305 216
 
< 0.1%
Other values (1120355) 2842801
99.9%
ValueCountFrequency (%)
-124.548074 2
 
< 0.1%
-124.517744 1
 
< 0.1%
-124.511949 1
 
< 0.1%
-124.497585 1
 
< 0.1%
-124.497567 2
 
< 0.1%
-124.49747 1
 
< 0.1%
-124.497448 2
 
< 0.1%
-124.497438 1
 
< 0.1%
-124.49742 5
< 0.1%
-124.49741 1
 
< 0.1%
ValueCountFrequency (%)
-67.113167 1
< 0.1%
-67.403551 1
< 0.1%
-67.48413 1
< 0.1%
-67.606864 1
< 0.1%
-67.606875 1
< 0.1%
-67.614387 1
< 0.1%
-67.626576 1
< 0.1%
-67.70337 1
< 0.1%
-67.739696 1
< 0.1%
-67.78734 1
< 0.1%

End_Lat
Real number (ℝ)

Distinct1080811
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.245321
Minimum24.566013
Maximum49.075
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.7 MiB
2023-02-28T17:09:27.060556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum24.566013
5-th percentile25.958574
Q133.446278
median36.097987
Q340.161049
95-th percentile45.090519
Maximum49.075
Range24.508987
Interquartile range (IQR)6.7147713

Descriptive statistics

Standard deviation5.363873
Coefficient of variation (CV)0.14798801
Kurtosis-0.58700212
Mean36.245321
Median Absolute Deviation (MAD)3.3769205
Skewness-0.11465709
Sum1.0313033 × 108
Variance28.771133
MonotonicityNot monotonic
2023-02-28T17:09:27.136113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.701774 755
 
< 0.1%
25.684322 709
 
< 0.1%
25.73316 589
 
< 0.1%
28.449928 586
 
< 0.1%
28.45019 581
 
< 0.1%
25.924771 576
 
< 0.1%
28.450015 567
 
< 0.1%
25.686252 548
 
< 0.1%
37.55119 525
 
< 0.1%
35.842775 522
 
< 0.1%
Other values (1080801) 2839384
99.8%
ValueCountFrequency (%)
24.566013 1
< 0.1%
24.570107 1
< 0.1%
24.57011 1
< 0.1%
24.57018 1
< 0.1%
24.57036 1
< 0.1%
24.570461 1
< 0.1%
24.57124 1
< 0.1%
24.57126 1
< 0.1%
24.571309 1
< 0.1%
24.571389 1
< 0.1%
ValueCountFrequency (%)
49.075 1
 
< 0.1%
49.00214 1
 
< 0.1%
49.00076 3
< 0.1%
49.00056 1
 
< 0.1%
48.999922 1
 
< 0.1%
48.99928 1
 
< 0.1%
48.999157 1
 
< 0.1%
48.999132 1
 
< 0.1%
48.99899 1
 
< 0.1%
48.998901 2
< 0.1%

End_Lng
Real number (ℝ)

Distinct1105404
Distinct (%)38.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-97.114387
Minimum-124.54575
Maximum-67.109242
Zeros0
Zeros (%)0.0%
Negative2845342
Negative (%)100.0%
Memory size21.7 MiB
2023-02-28T17:09:27.216947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-124.54575
5-th percentile-122.32599
Q1-118.03333
median-92.417718
Q3-80.373383
95-th percentile-74.173778
Maximum-67.109242
Range57.436506
Interquartile range (IQR)37.659948

Descriptive statistics

Standard deviation18.317632
Coefficient of variation (CV)-0.18861914
Kurtosis-1.6494699
Mean-97.114387
Median Absolute Deviation (MAD)15.437678
Skewness-0.23888876
Sum-2.7632364 × 108
Variance335.53566
MonotonicityNot monotonic
2023-02-28T17:09:27.287368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-80.334179 755
 
< 0.1%
-80.416621 709
 
< 0.1%
-80.336612 589
 
< 0.1%
-81.477219 586
 
< 0.1%
-81.399777 583
 
< 0.1%
-80.293318 576
 
< 0.1%
-81.471375 565
 
< 0.1%
-80.416521 547
 
< 0.1%
-77.475128 525
 
< 0.1%
-78.680237 522
 
< 0.1%
Other values (1105394) 2839385
99.8%
ValueCountFrequency (%)
-124.545748 2
 
< 0.1%
-124.509263 1
 
< 0.1%
-124.497829 1
 
< 0.1%
-124.497478 2
 
< 0.1%
-124.49747 1
 
< 0.1%
-124.497438 2
 
< 0.1%
-124.497421 1
 
< 0.1%
-124.497419 5
< 0.1%
-124.49741 1
 
< 0.1%
-124.497357 1
 
< 0.1%
ValueCountFrequency (%)
-67.109242 1
< 0.1%
-67.40355 1
< 0.1%
-67.48413 1
< 0.1%
-67.606864 1
< 0.1%
-67.62034 1
< 0.1%
-67.626576 1
< 0.1%
-67.626605 1
< 0.1%
-67.706448 1
< 0.1%
-67.739817 1
< 0.1%
-67.78734 1
< 0.1%

Distance(mi)
Real number (ℝ)

Distinct14165
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.70267789
Minimum0
Maximum155.186
Zeros385441
Zeros (%)13.5%
Negative0
Negative (%)0.0%
Memory size21.7 MiB
2023-02-28T17:09:27.364657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.052
median0.244
Q30.764
95-th percentile2.894
Maximum155.186
Range155.186
Interquartile range (IQR)0.712

Descriptive statistics

Standard deviation1.5603608
Coefficient of variation (CV)2.2205919
Kurtosis806.72437
Mean0.70267789
Median Absolute Deviation (MAD)0.238
Skewness16.670835
Sum1999358.9
Variance2.4347259
MonotonicityNot monotonic
2023-02-28T17:09:27.438718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 385441
 
13.5%
0.008 9262
 
0.3%
0.009 8978
 
0.3%
0.01 8737
 
0.3%
0.007 7846
 
0.3%
0.011 7319
 
0.3%
0.03 6937
 
0.2%
0.012 6894
 
0.2%
0.028 6789
 
0.2%
0.024 6777
 
0.2%
Other values (14155) 2390362
84.0%
ValueCountFrequency (%)
0 385441
13.5%
0.001 4897
 
0.2%
0.002 2605
 
0.1%
0.003 3388
 
0.1%
0.004 4670
 
0.2%
0.005 5608
 
0.2%
0.006 6687
 
0.2%
0.007 7846
 
0.3%
0.008 9262
 
0.3%
0.009 8978
 
0.3%
ValueCountFrequency (%)
155.186 1
< 0.1%
153.663 1
< 0.1%
152.543 1
< 0.1%
151.525 1
< 0.1%
150.138 1
< 0.1%
149.69 1
< 0.1%
149.687 1
< 0.1%
143.242 1
< 0.1%
138.562 1
< 0.1%
137.618 1
< 0.1%

Description
Categorical

Distinct1174563
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Memory size21.7 MiB
A crash has occurred causing no to minimum delays. Use caution.
 
7978
A crash has occurred use caution.
 
2531
An unconfirmed report of a crash has been received. Use caution.
 
2308
Hazardous debris is causing no to minimum delays. Use caution.
 
2095
At I-15 - Accident.
 
2070
Other values (1174558)
2828360 

Length

Max length577
Median length375
Mean length66.621769
Min length2

Characters and Unicode

Total characters189561718
Distinct characters98
Distinct categories15 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique790550 ?
Unique (%)27.8%

Sample

1st rowBetween Sawmill Rd/Exit 20 and OH-315/Olentangy Riv Rd/Exit 22 - Accident.
2nd rowAt OH-4/OH-235/Exit 41 - Accident.
3rd rowAt I-71/US-50/Exit 1 - Accident.
4th rowAt Dart Ave/Exit 21 - Accident.
5th rowAt Mitchell Ave/Exit 6 - Accident.

Common Values

ValueCountFrequency (%)
A crash has occurred causing no to minimum delays. Use caution. 7978
 
0.3%
A crash has occurred use caution. 2531
 
0.1%
An unconfirmed report of a crash has been received. Use caution. 2308
 
0.1%
Hazardous debris is causing no to minimum delays. Use caution. 2095
 
0.1%
At I-15 - Accident. 2070
 
0.1%
A disabled vehicle is creating a hazard causing no to minimum delays. Use caution. 1912
 
0.1%
At I-5 - Accident. 1907
 
0.1%
At I-405/San Diego Fwy - Accident. 1769
 
0.1%
At I-605 - Accident. 1486
 
0.1%
Incident on I-95 NB near I-95 Drive with caution. 1304
 
< 0.1%
Other values (1174553) 2819982
99.1%

Length

2023-02-28T17:09:27.607887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
accident 1798430
 
5.3%
to 1650005
 
4.9%
on 1598467
 
4.8%
1564037
 
4.7%
at 916875
 
2.7%
near 878801
 
2.6%
incident 856768
 
2.5%
from 796136
 
2.4%
due 782840
 
2.3%
rd 735640
 
2.2%
Other values (162605) 22044277
65.6%

Most occurring characters

ValueCountFrequency (%)
30776871
 
16.2%
t 11717120
 
6.2%
e 10644059
 
5.6%
n 9885855
 
5.2%
o 8424876
 
4.4%
i 8301886
 
4.4%
a 7363979
 
3.9%
c 7074776
 
3.7%
d 6570628
 
3.5%
r 5919544
 
3.1%
Other values (88) 82882124
43.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 98347096
51.9%
Uppercase Letter 34299454
 
18.1%
Space Separator 30777852
 
16.2%
Decimal Number 12986970
 
6.9%
Other Punctuation 5243233
 
2.8%
Dash Punctuation 5217447
 
2.8%
Open Punctuation 1344559
 
0.7%
Close Punctuation 1344492
 
0.7%
Math Symbol 365
 
< 0.1%
Modifier Symbol 102
 
< 0.1%
Other values (5) 148
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 11717120
11.9%
e 10644059
10.8%
n 9885855
10.1%
o 8424876
8.6%
i 8301886
8.4%
a 7363979
 
7.5%
c 7074776
 
7.2%
d 6570628
 
6.7%
r 5919544
 
6.0%
l 3533392
 
3.6%
Other values (19) 18910981
19.2%
Uppercase Letter
ValueCountFrequency (%)
A 4238891
12.4%
S 3746799
 
10.9%
I 3061578
 
8.9%
E 2940823
 
8.6%
R 2604603
 
7.6%
D 1817116
 
5.3%
N 1709487
 
5.0%
C 1555252
 
4.5%
L 1501320
 
4.4%
B 1449485
 
4.2%
Other values (16) 9674100
28.2%
Other Punctuation
ValueCountFrequency (%)
. 3211573
61.3%
/ 1897673
36.2%
: 65712
 
1.3%
' 26434
 
0.5%
; 25138
 
0.5%
* 7522
 
0.1%
& 5856
 
0.1%
# 2864
 
0.1%
@ 305
 
< 0.1%
? 92
 
< 0.1%
Other values (4) 64
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 2269949
17.5%
5 1571097
12.1%
2 1555128
12.0%
0 1459676
11.2%
4 1186461
9.1%
9 1130460
8.7%
3 1026620
7.9%
6 945893
7.3%
8 940013
7.2%
7 901673
 
6.9%
Open Punctuation
ValueCountFrequency (%)
( 1239926
92.2%
[ 101888
 
7.6%
{ 2745
 
0.2%
Close Punctuation
ValueCountFrequency (%)
) 1239866
92.2%
] 101881
 
7.6%
} 2745
 
0.2%
Math Symbol
ValueCountFrequency (%)
+ 161
44.1%
= 135
37.0%
~ 69
18.9%
Space Separator
ValueCountFrequency (%)
30776871
> 99.9%
  981
 
< 0.1%
Modifier Symbol
ValueCountFrequency (%)
^ 100
98.0%
` 2
 
2.0%
Dash Punctuation
ValueCountFrequency (%)
- 5217447
100.0%
Control
ValueCountFrequency (%)
96
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 42
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 6
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 2
100.0%
Other Symbol
ValueCountFrequency (%)
� 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 132646550
70.0%
Common 56915168
30.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 11717120
 
8.8%
e 10644059
 
8.0%
n 9885855
 
7.5%
o 8424876
 
6.4%
i 8301886
 
6.3%
a 7363979
 
5.6%
c 7074776
 
5.3%
d 6570628
 
5.0%
r 5919544
 
4.5%
A 4238891
 
3.2%
Other values (45) 52504936
39.6%
Common
ValueCountFrequency (%)
30776871
54.1%
- 5217447
 
9.2%
. 3211573
 
5.6%
1 2269949
 
4.0%
/ 1897673
 
3.3%
5 1571097
 
2.8%
2 1555128
 
2.7%
0 1459676
 
2.6%
( 1239926
 
2.2%
) 1239866
 
2.2%
Other values (33) 6475962
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 189560715
> 99.9%
None 997
 
< 0.1%
Punctuation 4
 
< 0.1%
Specials 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
30776871
 
16.2%
t 11717120
 
6.2%
e 10644059
 
5.6%
n 9885855
 
5.2%
o 8424876
 
4.4%
i 8301886
 
4.4%
a 7363979
 
3.9%
c 7074776
 
3.7%
d 6570628
 
3.5%
r 5919544
 
3.1%
Other values (81) 82881121
43.7%
None
ValueCountFrequency (%)
  981
98.4%
ñ 8
 
0.8%
é 7
 
0.7%
í 1
 
0.1%
Punctuation
ValueCountFrequency (%)
’ 2
50.0%
• 2
50.0%
Specials
ValueCountFrequency (%)
� 2
100.0%

Number
Real number (ℝ)

MISSING  SKEWED 

Distinct46402
Distinct (%)4.2%
Missing1743911
Missing (%)61.3%
Infinite0
Infinite (%)0.0%
Mean8089.4081
Minimum0
Maximum9999997
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size21.7 MiB
2023-02-28T17:09:27.690972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile108
Q11270
median4007
Q39567
95-th percentile28661.5
Maximum9999997
Range9999997
Interquartile range (IQR)8297

Descriptive statistics

Standard deviation18360.094
Coefficient of variation (CV)2.2696462
Kurtosis79967.464
Mean8089.4081
Median Absolute Deviation (MAD)3309
Skewness156.94502
Sum8.9099249 × 109
Variance3.3709305 × 108
MonotonicityNot monotonic
2023-02-28T17:09:27.768218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 10662
 
0.4%
2 8747
 
0.3%
101 6706
 
0.2%
100 6187
 
0.2%
198 2617
 
0.1%
298 2270
 
0.1%
200 2228
 
0.1%
201 2224
 
0.1%
398 2191
 
0.1%
199 2116
 
0.1%
Other values (46392) 1055483
37.1%
(Missing) 1743911
61.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 10662
0.4%
2 8747
0.3%
3 622
 
< 0.1%
4 557
 
< 0.1%
5 336
 
< 0.1%
6 306
 
< 0.1%
7 256
 
< 0.1%
8 301
 
< 0.1%
9 269
 
< 0.1%
ValueCountFrequency (%)
9999997 1
< 0.1%
961061 1
< 0.1%
961052 1
< 0.1%
961051 2
< 0.1%
961043 1
< 0.1%
961005 1
< 0.1%
942501 1
< 0.1%
941996 1
< 0.1%
940884 1
< 0.1%
852564 1
< 0.1%

Street
Categorical

Distinct159651
Distinct (%)5.6%
Missing2
Missing (%)< 0.1%
Memory size21.7 MiB
I-95 N
 
39853
I-5 N
 
39402
I-95 S
 
36425
I-5 S
 
30229
I-10 E
 
26164
Other values (159646)
2673267 

Length

Max length51
Median length46
Mean length11.162217
Min length2

Characters and Unicode

Total characters31760302
Distinct characters72
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64154 ?
Unique (%)2.3%

Sample

1st rowOuterbelt E
2nd rowI-70 E
3rd rowI-75 S
4th rowI-77 N
5th rowI-75 S

Common Values

ValueCountFrequency (%)
I-95 N 39853
 
1.4%
I-5 N 39402
 
1.4%
I-95 S 36425
 
1.3%
I-5 S 30229
 
1.1%
I-10 E 26164
 
0.9%
I-10 W 25298
 
0.9%
I-80 W 17545
 
0.6%
I-80 E 16873
 
0.6%
I-405 N 13708
 
0.5%
I-15 N 12675
 
0.4%
Other values (159641) 2587168
90.9%

Length

2023-02-28T17:09:27.862025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
n 514849
 
7.1%
s 512084
 
7.1%
w 394072
 
5.5%
e 392955
 
5.5%
rd 344344
 
4.8%
ave 228318
 
3.2%
st 215172
 
3.0%
fwy 149795
 
2.1%
highway 149101
 
2.1%
blvd 112666
 
1.6%
Other values (43749) 4195437
58.2%

Most occurring characters

ValueCountFrequency (%)
5464883
 
17.2%
e 1620891
 
5.1%
a 1390354
 
4.4%
S 1151559
 
3.6%
t 1124812
 
3.5%
- 1076858
 
3.4%
r 1050604
 
3.3%
o 1041312
 
3.3%
n 1002552
 
3.2%
l 939981
 
3.0%
Other values (62) 15896496
50.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 14683462
46.2%
Uppercase Letter 7354331
23.2%
Space Separator 5464883
 
17.2%
Decimal Number 3163551
 
10.0%
Dash Punctuation 1076858
 
3.4%
Other Punctuation 16875
 
0.1%
Other Symbol 342
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1620891
11.0%
a 1390354
 
9.5%
t 1124812
 
7.7%
r 1050604
 
7.2%
o 1041312
 
7.1%
n 1002552
 
6.8%
l 939981
 
6.4%
i 913631
 
6.2%
d 897396
 
6.1%
y 797274
 
5.4%
Other values (18) 3904655
26.6%
Uppercase Letter
ValueCountFrequency (%)
S 1151559
15.7%
I 819857
11.1%
N 655079
8.9%
W 606436
 
8.2%
E 556151
 
7.6%
A 488039
 
6.6%
R 479994
 
6.5%
C 373251
 
5.1%
H 361322
 
4.9%
B 313233
 
4.3%
Other values (16) 1549410
21.1%
Decimal Number
ValueCountFrequency (%)
5 493665
15.6%
0 451804
14.3%
1 442418
14.0%
9 319319
10.1%
4 295166
9.3%
2 266844
8.4%
8 262531
8.3%
7 229622
7.3%
6 227316
7.2%
3 174866
 
5.5%
Other Punctuation
ValueCountFrequency (%)
' 11457
67.9%
. 4230
 
25.1%
/ 1186
 
7.0%
& 1
 
< 0.1%
; 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
5464883
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1076858
100.0%
Other Symbol
ValueCountFrequency (%)
� 342
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 22037793
69.4%
Common 9722509
30.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1620891
 
7.4%
a 1390354
 
6.3%
S 1151559
 
5.2%
t 1124812
 
5.1%
r 1050604
 
4.8%
o 1041312
 
4.7%
n 1002552
 
4.5%
l 939981
 
4.3%
i 913631
 
4.1%
d 897396
 
4.1%
Other values (44) 10904701
49.5%
Common
ValueCountFrequency (%)
5464883
56.2%
- 1076858
 
11.1%
5 493665
 
5.1%
0 451804
 
4.6%
1 442418
 
4.6%
9 319319
 
3.3%
4 295166
 
3.0%
2 266844
 
2.7%
8 262531
 
2.7%
7 229622
 
2.4%
Other values (8) 419399
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31759462
> 99.9%
None 498
 
< 0.1%
Specials 342
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5464883
 
17.2%
e 1620891
 
5.1%
a 1390354
 
4.4%
S 1151559
 
3.6%
t 1124812
 
3.5%
- 1076858
 
3.4%
r 1050604
 
3.3%
o 1041312
 
3.3%
n 1002552
 
3.2%
l 939981
 
3.0%
Other values (59) 15895656
50.1%
None
ValueCountFrequency (%)
é 489
98.2%
ñ 9
 
1.8%
Specials
ValueCountFrequency (%)
� 342
100.0%

Side
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.7 MiB
R
2353309 
L
492032 
N
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2845342
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowR
2nd rowR
3rd rowR
4th rowR
5th rowR

Common Values

ValueCountFrequency (%)
R 2353309
82.7%
L 492032
 
17.3%
N 1
 
< 0.1%

Length

2023-02-28T17:09:27.930312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T17:09:27.987570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
r 2353309
82.7%
l 492032
 
17.3%
n 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
R 2353309
82.7%
L 492032
 
17.3%
N 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2845342
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 2353309
82.7%
L 492032
 
17.3%
N 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 2845342
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 2353309
82.7%
L 492032
 
17.3%
N 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2845342
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 2353309
82.7%
L 492032
 
17.3%
N 1
 
< 0.1%

City
Categorical

Distinct11681
Distinct (%)0.4%
Missing137
Missing (%)< 0.1%
Memory size21.7 MiB
Miami
 
106966
Los Angeles
 
68956
Orlando
 
54691
Dallas
 
41979
Houston
 
39448
Other values (11676)
2533165 

Length

Max length30
Median length26
Mean length8.8105012
Min length3

Characters and Unicode

Total characters25067682
Distinct characters65
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1110 ?
Unique (%)< 0.1%

Sample

1st rowDublin
2nd rowDayton
3rd rowCincinnati
4th rowAkron
5th rowCincinnati

Common Values

ValueCountFrequency (%)
Miami 106966
 
3.8%
Los Angeles 68956
 
2.4%
Orlando 54691
 
1.9%
Dallas 41979
 
1.5%
Houston 39448
 
1.4%
Charlotte 33152
 
1.2%
Sacramento 32559
 
1.1%
San Diego 26627
 
0.9%
Raleigh 22840
 
0.8%
Minneapolis 22768
 
0.8%
Other values (11671) 2395219
84.2%

Length

2023-02-28T17:09:28.047068image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
miami 119442
 
3.2%
san 87961
 
2.4%
los 74220
 
2.0%
city 69201
 
1.9%
angeles 69027
 
1.9%
orlando 54691
 
1.5%
dallas 42171
 
1.1%
saint 40075
 
1.1%
houston 39455
 
1.1%
beach 39263
 
1.1%
Other values (9456) 3086433
82.9%

Most occurring characters

ValueCountFrequency (%)
a 2502556
 
10.0%
e 2221044
 
8.9%
n 1890139
 
7.5%
o 1878078
 
7.5%
l 1661139
 
6.6%
i 1634406
 
6.5%
r 1492423
 
6.0%
t 1316077
 
5.3%
s 1161930
 
4.6%
876734
 
3.5%
Other values (55) 8433156
33.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 20468277
81.7%
Uppercase Letter 3721941
 
14.8%
Space Separator 876734
 
3.5%
Dash Punctuation 320
 
< 0.1%
Decimal Number 255
 
< 0.1%
Other Punctuation 155
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2502556
12.2%
e 2221044
10.9%
n 1890139
9.2%
o 1878078
9.2%
l 1661139
8.1%
i 1634406
8.0%
r 1492423
 
7.3%
t 1316077
 
6.4%
s 1161930
 
5.7%
d 645556
 
3.2%
Other values (16) 4064929
19.9%
Uppercase Letter
ValueCountFrequency (%)
S 392746
 
10.6%
C 362436
 
9.7%
M 333442
 
9.0%
L 266542
 
7.2%
B 259083
 
7.0%
P 242493
 
6.5%
A 220680
 
5.9%
H 200735
 
5.4%
R 187703
 
5.0%
O 160396
 
4.3%
Other values (16) 1095685
29.4%
Decimal Number
ValueCountFrequency (%)
4 162
63.5%
5 40
 
15.7%
7 19
 
7.5%
1 16
 
6.3%
6 5
 
2.0%
8 3
 
1.2%
2 3
 
1.2%
3 3
 
1.2%
0 2
 
0.8%
9 2
 
0.8%
Space Separator
ValueCountFrequency (%)
876734
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 320
100.0%
Other Punctuation
ValueCountFrequency (%)
. 155
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 24190218
96.5%
Common 877464
 
3.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2502556
 
10.3%
e 2221044
 
9.2%
n 1890139
 
7.8%
o 1878078
 
7.8%
l 1661139
 
6.9%
i 1634406
 
6.8%
r 1492423
 
6.2%
t 1316077
 
5.4%
s 1161930
 
4.8%
d 645556
 
2.7%
Other values (42) 7786870
32.2%
Common
ValueCountFrequency (%)
876734
99.9%
- 320
 
< 0.1%
4 162
 
< 0.1%
. 155
 
< 0.1%
5 40
 
< 0.1%
7 19
 
< 0.1%
1 16
 
< 0.1%
6 5
 
< 0.1%
8 3
 
< 0.1%
2 3
 
< 0.1%
Other values (3) 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25067682
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2502556
 
10.0%
e 2221044
 
8.9%
n 1890139
 
7.5%
o 1878078
 
7.5%
l 1661139
 
6.6%
i 1634406
 
6.5%
r 1492423
 
6.0%
t 1316077
 
5.3%
s 1161930
 
4.6%
876734
 
3.5%
Other values (55) 8433156
33.6%

County
Categorical

Distinct1707
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.7 MiB
Los Angeles
234122 
Miami-Dade
 
143939
Orange
 
114917
San Bernardino
 
55018
Dallas
 
50050
Other values (1702)
2247296 

Length

Max length20
Median length16
Mean length8.1958745
Min length3

Characters and Unicode

Total characters23320066
Distinct characters57
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66 ?
Unique (%)< 0.1%

Sample

1st rowFranklin
2nd rowMontgomery
3rd rowHamilton
4th rowSummit
5th rowHamilton

Common Values

ValueCountFrequency (%)
Los Angeles 234122
 
8.2%
Miami-Dade 143939
 
5.1%
Orange 114917
 
4.0%
San Bernardino 55018
 
1.9%
Dallas 50050
 
1.8%
San Diego 48366
 
1.7%
Sacramento 46708
 
1.6%
Harris 42559
 
1.5%
Riverside 42176
 
1.5%
Montgomery 41476
 
1.5%
Other values (1697) 2026011
71.2%

Length

2023-02-28T17:09:28.481356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
los 234123
 
6.6%
angeles 234122
 
6.6%
san 145652
 
4.1%
miami-dade 143939
 
4.1%
orange 114917
 
3.3%
bernardino 55018
 
1.6%
dallas 50050
 
1.4%
diego 48366
 
1.4%
sacramento 46708
 
1.3%
santa 43738
 
1.2%
Other values (1668) 2418952
68.4%

Most occurring characters

ValueCountFrequency (%)
a 2602218
 
11.2%
e 2445101
 
10.5%
n 1899466
 
8.1%
o 1619413
 
6.9%
r 1442849
 
6.2%
i 1307901
 
5.6%
s 1284451
 
5.5%
l 1157072
 
5.0%
t 785883
 
3.4%
690243
 
3.0%
Other values (47) 8085469
34.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 18767444
80.5%
Uppercase Letter 3684912
 
15.8%
Space Separator 690243
 
3.0%
Dash Punctuation 144233
 
0.6%
Other Punctuation 33203
 
0.1%
Other Symbol 31
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2602218
13.9%
e 2445101
13.0%
n 1899466
10.1%
o 1619413
8.6%
r 1442849
7.7%
i 1307901
 
7.0%
s 1284451
 
6.8%
l 1157072
 
6.2%
t 785883
 
4.2%
g 678325
 
3.6%
Other values (17) 3544765
18.9%
Uppercase Letter
ValueCountFrequency (%)
M 414228
11.2%
S 394862
10.7%
L 372320
10.1%
D 369042
10.0%
A 336667
9.1%
C 287055
 
7.8%
B 212764
 
5.8%
H 173937
 
4.7%
O 173249
 
4.7%
P 139348
 
3.8%
Other values (15) 811440
22.0%
Other Punctuation
ValueCountFrequency (%)
. 18431
55.5%
' 14772
44.5%
Space Separator
ValueCountFrequency (%)
690243
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 144233
100.0%
Other Symbol
ValueCountFrequency (%)
� 31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 22452356
96.3%
Common 867710
 
3.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2602218
 
11.6%
e 2445101
 
10.9%
n 1899466
 
8.5%
o 1619413
 
7.2%
r 1442849
 
6.4%
i 1307901
 
5.8%
s 1284451
 
5.7%
l 1157072
 
5.2%
t 785883
 
3.5%
g 678325
 
3.0%
Other values (42) 7229677
32.2%
Common
ValueCountFrequency (%)
690243
79.5%
- 144233
 
16.6%
. 18431
 
2.1%
' 14772
 
1.7%
� 31
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23319942
> 99.9%
None 93
 
< 0.1%
Specials 31
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2602218
 
11.2%
e 2445101
 
10.5%
n 1899466
 
8.1%
o 1619413
 
6.9%
r 1442849
 
6.2%
i 1307901
 
5.6%
s 1284451
 
5.5%
l 1157072
 
5.0%
t 785883
 
3.4%
690243
 
3.0%
Other values (45) 8085345
34.7%
None
ValueCountFrequency (%)
ñ 93
100.0%
Specials
ValueCountFrequency (%)
� 31
100.0%

State
Categorical

Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.7 MiB
CA
795868 
FL
401388 
TX
149037 
OR
126341 
VA
 
113535
Other values (44)
1259173 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters5690684
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOH
2nd rowOH
3rd rowOH
4th rowOH
5th rowOH

Common Values

ValueCountFrequency (%)
CA 795868
28.0%
FL 401388
14.1%
TX 149037
 
5.2%
OR 126341
 
4.4%
VA 113535
 
4.0%
NY 108049
 
3.8%
PA 99975
 
3.5%
MN 97185
 
3.4%
NC 91362
 
3.2%
SC 89216
 
3.1%
Other values (39) 773386
27.2%

Length

2023-02-28T17:09:28.545801image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca 795868
28.0%
fl 401388
14.1%
tx 149037
 
5.2%
or 126341
 
4.4%
va 113535
 
4.0%
ny 108049
 
3.8%
pa 99975
 
3.5%
mn 97185
 
3.4%
nc 91362
 
3.2%
sc 89216
 
3.1%
Other values (39) 773386
27.2%

Most occurring characters

ValueCountFrequency (%)
A 1232010
21.6%
C 1040681
18.3%
L 515047
9.1%
N 440972
 
7.7%
F 401388
 
7.1%
T 296934
 
5.2%
M 267985
 
4.7%
O 214529
 
3.8%
X 149037
 
2.6%
I 142296
 
2.5%
Other values (14) 989805
17.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5690684
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1232010
21.6%
C 1040681
18.3%
L 515047
9.1%
N 440972
 
7.7%
F 401388
 
7.1%
T 296934
 
5.2%
M 267985
 
4.7%
O 214529
 
3.8%
X 149037
 
2.6%
I 142296
 
2.5%
Other values (14) 989805
17.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 5690684
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1232010
21.6%
C 1040681
18.3%
L 515047
9.1%
N 440972
 
7.7%
F 401388
 
7.1%
T 296934
 
5.2%
M 267985
 
4.7%
O 214529
 
3.8%
X 149037
 
2.6%
I 142296
 
2.5%
Other values (14) 989805
17.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5690684
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1232010
21.6%
C 1040681
18.3%
L 515047
9.1%
N 440972
 
7.7%
F 401388
 
7.1%
T 296934
 
5.2%
M 267985
 
4.7%
O 214529
 
3.8%
X 149037
 
2.6%
I 142296
 
2.5%
Other values (14) 989805
17.4%

Zipcode
Categorical

Distinct363085
Distinct (%)12.8%
Missing1319
Missing (%)< 0.1%
Memory size21.7 MiB
91761
 
6162
33186
 
5248
92407
 
4528
92507
 
4527
91706
 
4471
Other values (363080)
2819087 

Length

Max length10
Median length5
Mean length6.4097372
Min length5

Characters and Unicode

Total characters18229440
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique209614 ?
Unique (%)7.4%

Sample

1st row43017
2nd row45424
3rd row45203
4th row44311
5th row45217

Common Values

ValueCountFrequency (%)
91761 6162
 
0.2%
33186 5248
 
0.2%
92407 4528
 
0.2%
92507 4527
 
0.2%
91706 4471
 
0.2%
33183 3518
 
0.1%
92324 3491
 
0.1%
32819 3455
 
0.1%
33169 3439
 
0.1%
91765 3189
 
0.1%
Other values (363075) 2801995
98.5%

Length

2023-02-28T17:09:28.621860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
91761 6162
 
0.2%
33186 5248
 
0.2%
92407 4528
 
0.2%
92507 4527
 
0.2%
91706 4471
 
0.2%
33183 3518
 
0.1%
92324 3491
 
0.1%
32819 3455
 
0.1%
33169 3439
 
0.1%
91765 3189
 
0.1%
Other values (363075) 2801995
98.5%

Most occurring characters

ValueCountFrequency (%)
0 2252352
12.4%
2 2174309
11.9%
3 2173091
11.9%
1 2043786
11.2%
9 1788058
9.8%
5 1575164
8.6%
7 1519116
8.3%
4 1485471
8.1%
6 1243905
6.8%
8 1172323
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17427575
95.6%
Dash Punctuation 801865
 
4.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2252352
12.9%
2 2174309
12.5%
3 2173091
12.5%
1 2043786
11.7%
9 1788058
10.3%
5 1575164
9.0%
7 1519116
8.7%
4 1485471
8.5%
6 1243905
7.1%
8 1172323
6.7%
Dash Punctuation
ValueCountFrequency (%)
- 801865
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18229440
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2252352
12.4%
2 2174309
11.9%
3 2173091
11.9%
1 2043786
11.2%
9 1788058
9.8%
5 1575164
8.6%
7 1519116
8.3%
4 1485471
8.1%
6 1243905
6.8%
8 1172323
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18229440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2252352
12.4%
2 2174309
11.9%
3 2173091
11.9%
1 2043786
11.2%
9 1788058
9.8%
5 1575164
8.6%
7 1519116
8.3%
4 1485471
8.1%
6 1243905
6.8%
8 1172323
6.4%

Country
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.7 MiB
US
2845342 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters5690684
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS

Common Values

ValueCountFrequency (%)
US 2845342
100.0%

Length

2023-02-28T17:09:28.676256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T17:09:28.729156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
us 2845342
100.0%

Most occurring characters

ValueCountFrequency (%)
U 2845342
50.0%
S 2845342
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5690684
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 2845342
50.0%
S 2845342
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5690684
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 2845342
50.0%
S 2845342
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5690684
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 2845342
50.0%
S 2845342
50.0%

Timezone
Categorical

Distinct4
Distinct (%)< 0.1%
Missing3659
Missing (%)0.1%
Memory size21.7 MiB
US/Eastern
1221927 
US/Pacific
967094 
US/Central
488065 
US/Mountain
164597 

Length

Max length11
Median length10
Mean length10.057922
Min length10

Characters and Unicode

Total characters28581427
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUS/Eastern
2nd rowUS/Eastern
3rd rowUS/Eastern
4th rowUS/Eastern
5th rowUS/Eastern

Common Values

ValueCountFrequency (%)
US/Eastern 1221927
42.9%
US/Pacific 967094
34.0%
US/Central 488065
 
17.2%
US/Mountain 164597
 
5.8%
(Missing) 3659
 
0.1%

Length

2023-02-28T17:09:28.774597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T17:09:28.834889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
us/eastern 1221927
43.0%
us/pacific 967094
34.0%
us/central 488065
 
17.2%
us/mountain 164597
 
5.8%

Most occurring characters

ValueCountFrequency (%)
U 2841683
9.9%
S 2841683
9.9%
/ 2841683
9.9%
a 2841683
9.9%
i 2098785
 
7.3%
n 2039186
 
7.1%
c 1934188
 
6.8%
t 1874589
 
6.6%
e 1709992
 
6.0%
r 1709992
 
6.0%
Other values (9) 5847963
20.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 17214695
60.2%
Uppercase Letter 8525049
29.8%
Other Punctuation 2841683
 
9.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2841683
16.5%
i 2098785
12.2%
n 2039186
11.8%
c 1934188
11.2%
t 1874589
10.9%
e 1709992
9.9%
r 1709992
9.9%
s 1221927
7.1%
f 967094
 
5.6%
l 488065
 
2.8%
Other values (2) 329194
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
U 2841683
33.3%
S 2841683
33.3%
E 1221927
14.3%
P 967094
 
11.3%
C 488065
 
5.7%
M 164597
 
1.9%
Other Punctuation
ValueCountFrequency (%)
/ 2841683
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 25739744
90.1%
Common 2841683
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 2841683
11.0%
S 2841683
11.0%
a 2841683
11.0%
i 2098785
8.2%
n 2039186
7.9%
c 1934188
7.5%
t 1874589
7.3%
e 1709992
 
6.6%
r 1709992
 
6.6%
s 1221927
 
4.7%
Other values (8) 4626036
18.0%
Common
ValueCountFrequency (%)
/ 2841683
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28581427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 2841683
9.9%
S 2841683
9.9%
/ 2841683
9.9%
a 2841683
9.9%
i 2098785
 
7.3%
n 2039186
 
7.1%
c 1934188
 
6.8%
t 1874589
 
6.6%
e 1709992
 
6.0%
r 1709992
 
6.0%
Other values (9) 5847963
20.5%

Airport_Code
Categorical

Distinct2004
Distinct (%)0.1%
Missing9549
Missing (%)0.3%
Memory size21.7 MiB
KCQT
 
52790
KMIA
 
45740
KORL
 
39380
KOPF
 
38556
KTMB
 
36250
Other values (1999)
2623077 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters11343172
Distinct characters36
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)< 0.1%

Sample

1st rowKOSU
2nd rowKFFO
3rd rowKLUK
4th rowKAKR
5th rowKLUK

Common Values

ValueCountFrequency (%)
KCQT 52790
 
1.9%
KMIA 45740
 
1.6%
KORL 39380
 
1.4%
KOPF 38556
 
1.4%
KTMB 36250
 
1.3%
KEMT 29931
 
1.1%
KRDU 29322
 
1.0%
KFUL 28575
 
1.0%
KHHR 27305
 
1.0%
KBNA 27061
 
1.0%
Other values (1994) 2480883
87.2%

Length

2023-02-28T17:09:28.889974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kcqt 52790
 
1.9%
kmia 45740
 
1.6%
korl 39380
 
1.4%
kopf 38556
 
1.4%
ktmb 36250
 
1.3%
kemt 29931
 
1.1%
krdu 29322
 
1.0%
kful 28575
 
1.0%
khhr 27305
 
1.0%
kbna 27061
 
1.0%
Other values (1994) 2480883
87.5%

Most occurring characters

ValueCountFrequency (%)
K 2996056
26.4%
A 594595
 
5.2%
C 587310
 
5.2%
M 541139
 
4.8%
S 498222
 
4.4%
T 476820
 
4.2%
L 467659
 
4.1%
O 449386
 
4.0%
R 439134
 
3.9%
D 437371
 
3.9%
Other values (26) 3855480
34.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 11189338
98.6%
Decimal Number 153834
 
1.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
K 2996056
26.8%
A 594595
 
5.3%
C 587310
 
5.2%
M 541139
 
4.8%
S 498222
 
4.5%
T 476820
 
4.3%
L 467659
 
4.2%
O 449386
 
4.0%
R 439134
 
3.9%
D 437371
 
3.9%
Other values (16) 3701646
33.1%
Decimal Number
ValueCountFrequency (%)
2 33233
21.6%
6 25327
16.5%
4 23061
15.0%
3 16538
10.8%
7 13339
8.7%
0 12452
 
8.1%
9 10951
 
7.1%
1 10758
 
7.0%
8 4389
 
2.9%
5 3786
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 11189338
98.6%
Common 153834
 
1.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
K 2996056
26.8%
A 594595
 
5.3%
C 587310
 
5.2%
M 541139
 
4.8%
S 498222
 
4.5%
T 476820
 
4.3%
L 467659
 
4.2%
O 449386
 
4.0%
R 439134
 
3.9%
D 437371
 
3.9%
Other values (16) 3701646
33.1%
Common
ValueCountFrequency (%)
2 33233
21.6%
6 25327
16.5%
4 23061
15.0%
3 16538
10.8%
7 13339
8.7%
0 12452
 
8.1%
9 10951
 
7.1%
1 10758
 
7.0%
8 4389
 
2.9%
5 3786
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11343172
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
K 2996056
26.4%
A 594595
 
5.2%
C 587310
 
5.2%
M 541139
 
4.8%
S 498222
 
4.4%
T 476820
 
4.2%
L 467659
 
4.1%
O 449386
 
4.0%
R 439134
 
3.9%
D 437371
 
3.9%
Other values (26) 3855480
34.0%

Weather_Timestamp
Categorical

HIGH CARDINALITY  MISSING 

Distinct474214
Distinct (%)17.0%
Missing50736
Missing (%)1.8%
Memory size21.7 MiB
2021-12-17 14:53:00
 
640
2021-12-23 14:53:00
 
629
2021-01-26 15:53:00
 
598
2021-12-06 16:53:00
 
595
2021-12-03 16:53:00
 
593
Other values (474209)
2791551 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters53097514
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique186552 ?
Unique (%)6.7%

Sample

1st row2016-02-08 00:53:00
2nd row2016-02-08 05:58:00
3rd row2016-02-08 05:53:00
4th row2016-02-08 06:54:00
5th row2016-02-08 07:53:00

Common Values

ValueCountFrequency (%)
2021-12-17 14:53:00 640
 
< 0.1%
2021-12-23 14:53:00 629
 
< 0.1%
2021-01-26 15:53:00 598
 
< 0.1%
2021-12-06 16:53:00 595
 
< 0.1%
2021-12-03 16:53:00 593
 
< 0.1%
2021-12-17 17:53:00 572
 
< 0.1%
2021-11-30 16:53:00 569
 
< 0.1%
2021-12-15 16:53:00 567
 
< 0.1%
2021-12-03 14:53:00 565
 
< 0.1%
2021-12-07 16:53:00 560
 
< 0.1%
Other values (474204) 2788718
98.0%
(Missing) 50736
 
1.8%

Length

2023-02-28T17:09:28.960387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
16:53:00 95753
 
1.7%
15:53:00 94214
 
1.7%
14:53:00 88219
 
1.6%
17:53:00 85653
 
1.5%
13:53:00 75857
 
1.4%
12:53:00 66186
 
1.2%
18:53:00 59874
 
1.1%
07:53:00 55202
 
1.0%
11:53:00 50706
 
0.9%
08:53:00 46872
 
0.8%
Other values (3529) 4870676
87.1%

Most occurring characters

ValueCountFrequency (%)
0 13312181
25.1%
2 7541273
14.2%
1 7373996
13.9%
- 5589212
10.5%
: 5589212
10.5%
5 3411025
 
6.4%
2794606
 
5.3%
3 2200904
 
4.1%
6 1217216
 
2.3%
4 1064424
 
2.0%
Other values (3) 3003465
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39124484
73.7%
Dash Punctuation 5589212
 
10.5%
Other Punctuation 5589212
 
10.5%
Space Separator 2794606
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13312181
34.0%
2 7541273
19.3%
1 7373996
18.8%
5 3411025
 
8.7%
3 2200904
 
5.6%
6 1217216
 
3.1%
4 1064424
 
2.7%
7 1029347
 
2.6%
9 1012235
 
2.6%
8 961883
 
2.5%
Dash Punctuation
ValueCountFrequency (%)
- 5589212
100.0%
Other Punctuation
ValueCountFrequency (%)
: 5589212
100.0%
Space Separator
ValueCountFrequency (%)
2794606
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 53097514
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13312181
25.1%
2 7541273
14.2%
1 7373996
13.9%
- 5589212
10.5%
: 5589212
10.5%
5 3411025
 
6.4%
2794606
 
5.3%
3 2200904
 
4.1%
6 1217216
 
2.3%
4 1064424
 
2.0%
Other values (3) 3003465
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53097514
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13312181
25.1%
2 7541273
14.2%
1 7373996
13.9%
- 5589212
10.5%
: 5589212
10.5%
5 3411025
 
6.4%
2794606
 
5.3%
3 2200904
 
4.1%
6 1217216
 
2.3%
4 1064424
 
2.0%
Other values (3) 3003465
 
5.7%

Temperature(F)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct788
Distinct (%)< 0.1%
Missing69274
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean61.793556
Minimum-89
Maximum196
Zeros984
Zeros (%)< 0.1%
Negative6444
Negative (%)0.2%
Memory size21.7 MiB
2023-02-28T17:09:29.023585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-89
5-th percentile29
Q150
median64
Q376
95-th percentile88.9
Maximum196
Range285
Interquartile range (IQR)26

Descriptive statistics

Standard deviation18.622629
Coefficient of variation (CV)0.30136847
Kurtosis0.030021552
Mean61.793556
Median Absolute Deviation (MAD)13
Skewness-0.49107947
Sum1.7154311 × 108
Variance346.80233
MonotonicityNot monotonic
2023-02-28T17:09:29.095802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73 64505
 
2.3%
77 63575
 
2.2%
75 60534
 
2.1%
72 59681
 
2.1%
68 58557
 
2.1%
63 58259
 
2.0%
64 57937
 
2.0%
70 57760
 
2.0%
66 56336
 
2.0%
59 56025
 
2.0%
Other values (778) 2182899
76.7%
(Missing) 69274
 
2.4%
ValueCountFrequency (%)
-89 2
< 0.1%
-77.8 1
 
< 0.1%
-58 1
 
< 0.1%
-50 1
 
< 0.1%
-40 1
 
< 0.1%
-33 1
 
< 0.1%
-30 1
 
< 0.1%
-29 4
< 0.1%
-28 2
< 0.1%
-27.9 2
< 0.1%
ValueCountFrequency (%)
196 3
 
< 0.1%
170.6 1
 
< 0.1%
168.8 1
 
< 0.1%
156 1
 
< 0.1%
144 1
 
< 0.1%
136 1
 
< 0.1%
129.2 1
 
< 0.1%
127.4 1
 
< 0.1%
120 8
< 0.1%
119 5
< 0.1%

Wind_Chill(F)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct897
Distinct (%)< 0.1%
Missing469643
Missing (%)16.5%
Infinite0
Infinite (%)0.0%
Mean59.658231
Minimum-89
Maximum196
Zeros1322
Zeros (%)< 0.1%
Negative20659
Negative (%)0.7%
Memory size21.7 MiB
2023-02-28T17:09:29.175992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-89
5-th percentile21
Q146
median63
Q376
95-th percentile88
Maximum196
Range285
Interquartile range (IQR)30

Descriptive statistics

Standard deviation21.160967
Coefficient of variation (CV)0.35470323
Kurtosis0.36821114
Mean59.658231
Median Absolute Deviation (MAD)14
Skewness-0.70827227
Sum1.4173 × 108
Variance447.78654
MonotonicityNot monotonic
2023-02-28T17:09:29.253057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73 56492
 
2.0%
77 53359
 
1.9%
75 52898
 
1.9%
72 52196
 
1.8%
63 50784
 
1.8%
64 50374
 
1.8%
70 50187
 
1.8%
79 48936
 
1.7%
66 48929
 
1.7%
68 48517
 
1.7%
Other values (887) 1863027
65.5%
(Missing) 469643
 
16.5%
ValueCountFrequency (%)
-89 2
< 0.1%
-80 1
 
< 0.1%
-65.9 1
 
< 0.1%
-59 1
 
< 0.1%
-58 1
 
< 0.1%
-53.5 4
< 0.1%
-53.1 1
 
< 0.1%
-52.3 1
 
< 0.1%
-51.7 1
 
< 0.1%
-51.5 2
< 0.1%
ValueCountFrequency (%)
196 3
 
< 0.1%
156 1
 
< 0.1%
144 1
 
< 0.1%
136 1
 
< 0.1%
120 8
 
< 0.1%
119 5
 
< 0.1%
117 11
 
< 0.1%
116 8
 
< 0.1%
115 13
 
< 0.1%
114 36
< 0.1%

Humidity(%)
Real number (ℝ)

Distinct100
Distinct (%)< 0.1%
Missing73092
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean64.365452
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.7 MiB
2023-02-28T17:09:29.334301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile23
Q148
median67
Q383
95-th percentile96
Maximum100
Range99
Interquartile range (IQR)35

Descriptive statistics

Standard deviation22.874568
Coefficient of variation (CV)0.35538581
Kurtosis-0.68272358
Mean64.365452
Median Absolute Deviation (MAD)17
Skewness-0.41576294
Sum1.7843712 × 108
Variance523.24587
MonotonicityNot monotonic
2023-02-28T17:09:29.412552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93 103607
 
3.6%
100 96907
 
3.4%
87 60236
 
2.1%
90 57587
 
2.0%
89 53396
 
1.9%
86 47702
 
1.7%
82 46793
 
1.6%
81 45330
 
1.6%
67 45180
 
1.6%
96 45111
 
1.6%
Other values (90) 2170401
76.3%
(Missing) 73092
 
2.6%
ValueCountFrequency (%)
1 26
 
< 0.1%
2 121
 
< 0.1%
3 341
 
< 0.1%
4 943
 
< 0.1%
5 1804
 
0.1%
6 2408
0.1%
7 3457
0.1%
8 4182
0.1%
9 4758
0.2%
10 5601
0.2%
ValueCountFrequency (%)
100 96907
3.4%
99 4306
 
0.2%
98 2329
 
0.1%
97 29111
 
1.0%
96 45111
1.6%
95 3342
 
0.1%
94 39650
 
1.4%
93 103607
3.6%
92 23444
 
0.8%
91 12828
 
0.5%

Pressure(in)
Real number (ℝ)

Distinct1068
Distinct (%)< 0.1%
Missing59200
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean29.472344
Minimum0
Maximum58.9
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size21.7 MiB
2023-02-28T17:09:29.488645image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27.25
Q129.31
median29.82
Q330.01
95-th percentile30.21
Maximum58.9
Range58.9
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation1.0452865
Coefficient of variation (CV)0.03546669
Kurtosis16.723945
Mean29.472344
Median Absolute Deviation (MAD)0.26
Skewness-3.2575402
Sum82114137
Variance1.0926239
MonotonicityNot monotonic
2023-02-28T17:09:29.559030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.96 43668
 
1.5%
29.94 43165
 
1.5%
29.99 42978
 
1.5%
30.01 41736
 
1.5%
30.03 41477
 
1.5%
30.04 40651
 
1.4%
29.97 40395
 
1.4%
30 40178
 
1.4%
29.95 40084
 
1.4%
29.93 39474
 
1.4%
Other values (1058) 2372336
83.4%
(Missing) 59200
 
2.1%
ValueCountFrequency (%)
0 1
< 0.1%
0.02 1
< 0.1%
0.3 2
< 0.1%
2.99 2
< 0.1%
3.04 2
< 0.1%
16.72 2
< 0.1%
19.21 2
< 0.1%
19.24 2
< 0.1%
19.37 1
< 0.1%
19.48 1
< 0.1%
ValueCountFrequency (%)
58.9 1
 
< 0.1%
58.16 2
< 0.1%
58.13 1
 
< 0.1%
58.04 2
< 0.1%
57.74 1
 
< 0.1%
56.54 1
 
< 0.1%
56.31 1
 
< 0.1%
52.76 2
< 0.1%
38.94 3
< 0.1%
32.87 1
 
< 0.1%

Visibility(mi)
Real number (ℝ)

Distinct76
Distinct (%)< 0.1%
Missing70546
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean9.0993913
Minimum0
Maximum140
Zeros3238
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size21.7 MiB
2023-02-28T17:09:29.632784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q110
median10
Q310
95-th percentile10
Maximum140
Range140
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.7175457
Coefficient of variation (CV)0.29865138
Kurtosis103.50643
Mean9.0993913
Median Absolute Deviation (MAD)0
Skewness3.1133741
Sum25248955
Variance7.3850549
MonotonicityNot monotonic
2023-02-28T17:09:29.701617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 2230276
78.4%
7 79649
 
2.8%
9 68817
 
2.4%
8 55955
 
2.0%
5 53933
 
1.9%
6 49051
 
1.7%
2 46160
 
1.6%
4 45437
 
1.6%
3 44012
 
1.5%
1 38445
 
1.4%
Other values (66) 63061
 
2.2%
(Missing) 70546
 
2.5%
ValueCountFrequency (%)
0 3238
 
0.1%
0.06 118
 
< 0.1%
0.1 268
 
< 0.1%
0.12 726
 
< 0.1%
0.19 12
 
< 0.1%
0.2 2870
 
0.1%
0.25 11359
0.4%
0.38 130
 
< 0.1%
0.4 25
 
< 0.1%
0.5 12290
0.4%
ValueCountFrequency (%)
140 2
 
< 0.1%
130 1
 
< 0.1%
120 4
 
< 0.1%
111 1
 
< 0.1%
110 1
 
< 0.1%
100 37
 
< 0.1%
90 9
 
< 0.1%
80 186
< 0.1%
75 33
 
< 0.1%
70 99
< 0.1%

Wind_Direction
Categorical

Distinct24
Distinct (%)< 0.1%
Missing73775
Missing (%)2.6%
Memory size21.7 MiB
CALM
433622 
S
 
169743
W
 
167830
WNW
 
145046
NW
 
141344
Other values (19)
1713982 

Length

Max length8
Median length5
Mean length2.7085573
Min length1

Characters and Unicode

Total characters7506948
Distinct characters22
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSW
2nd rowCalm
3rd rowCalm
4th rowCalm
5th rowWSW

Common Values

ValueCountFrequency (%)
CALM 433622
15.2%
S 169743
 
6.0%
W 167830
 
5.9%
WNW 145046
 
5.1%
NW 141344
 
5.0%
SSW 137282
 
4.8%
WSW 130734
 
4.6%
SW 128970
 
4.5%
SSE 125516
 
4.4%
NNW 124583
 
4.4%
Other values (14) 1066897
37.5%

Length

2023-02-28T17:09:29.773029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
calm 510156
18.4%
s 169743
 
6.1%
w 167830
 
6.1%
wnw 145046
 
5.2%
nw 141344
 
5.1%
ssw 137282
 
5.0%
wsw 130734
 
4.7%
sw 128970
 
4.7%
sse 125516
 
4.5%
nnw 124583
 
4.5%
Other values (13) 990363
35.7%

Most occurring characters

ValueCountFrequency (%)
W 1290808
17.2%
S 1206342
16.1%
N 1046938
13.9%
E 950572
12.7%
A 537804
7.2%
C 510156
 
6.8%
L 433622
 
5.8%
M 433622
 
5.8%
a 144111
 
1.9%
t 135911
 
1.8%
Other values (12) 817062
10.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6640457
88.5%
Lowercase Letter 866491
 
11.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 144111
16.6%
t 135911
15.7%
l 98763
11.4%
m 76534
8.8%
o 73553
8.5%
h 73553
8.5%
s 62358
7.2%
e 61468
7.1%
r 56607
 
6.5%
u 39175
 
4.5%
Other values (2) 44458
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
W 1290808
19.4%
S 1206342
18.2%
N 1046938
15.8%
E 950572
14.3%
A 537804
8.1%
C 510156
 
7.7%
L 433622
 
6.5%
M 433622
 
6.5%
V 126411
 
1.9%
R 104182
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 7506948
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
W 1290808
17.2%
S 1206342
16.1%
N 1046938
13.9%
E 950572
12.7%
A 537804
7.2%
C 510156
 
6.8%
L 433622
 
5.8%
M 433622
 
5.8%
a 144111
 
1.9%
t 135911
 
1.8%
Other values (12) 817062
10.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7506948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
W 1290808
17.2%
S 1206342
16.1%
N 1046938
13.9%
E 950572
12.7%
A 537804
7.2%
C 510156
 
6.8%
L 433622
 
5.8%
M 433622
 
5.8%
a 144111
 
1.9%
t 135911
 
1.8%
Other values (12) 817062
10.9%

Wind_Speed(mph)
Real number (ℝ)

MISSING  ZEROS 

Distinct136
Distinct (%)< 0.1%
Missing157944
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean7.3950442
Minimum0
Maximum1087
Zeros433636
Zeros (%)15.2%
Negative0
Negative (%)0.0%
Memory size21.7 MiB
2023-02-28T17:09:29.843332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.5
median7
Q310
95-th percentile17
Maximum1087
Range1087
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation5.527454
Coefficient of variation (CV)0.74745381
Kurtosis1508.0314
Mean7.3950442
Median Absolute Deviation (MAD)3
Skewness10.249727
Sum19873427
Variance30.552747
MonotonicityNot monotonic
2023-02-28T17:09:29.918313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 433636
15.2%
5 231000
 
8.1%
3 225664
 
7.9%
6 222502
 
7.8%
7 205667
 
7.2%
8 184037
 
6.5%
9 165127
 
5.8%
10 135850
 
4.8%
12 117003
 
4.1%
13 88872
 
3.1%
Other values (126) 678040
23.8%
(Missing) 157944
 
5.6%
ValueCountFrequency (%)
0 433636
15.2%
1 58
 
< 0.1%
1.2 116
 
< 0.1%
2 150
 
< 0.1%
2.3 251
 
< 0.1%
3 225664
7.9%
3.5 42429
 
1.5%
4.6 44989
 
1.6%
5 231000
8.1%
5.8 45427
 
1.6%
ValueCountFrequency (%)
1087 1
< 0.1%
984 1
< 0.1%
822.8 2
< 0.1%
812 1
< 0.1%
518 2
< 0.1%
471.8 1
< 0.1%
245.1 1
< 0.1%
243 1
< 0.1%
232 1
< 0.1%
211 1
< 0.1%

Precipitation(in)
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct230
Distinct (%)< 0.1%
Missing549458
Missing (%)19.3%
Infinite0
Infinite (%)0.0%
Mean0.0070169399
Minimum0
Maximum24
Zeros2104242
Zeros (%)74.0%
Negative0
Negative (%)0.0%
Memory size21.7 MiB
2023-02-28T17:09:29.991869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.02
Maximum24
Range24
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.093488312
Coefficient of variation (CV)13.323231
Kurtosis16665.256
Mean0.0070169399
Median Absolute Deviation (MAD)0
Skewness106.25894
Sum16110.08
Variance0.0087400644
MonotonicityNot monotonic
2023-02-28T17:09:30.065513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2104242
74.0%
0.01 54160
 
1.9%
0.02 26497
 
0.9%
0.03 17786
 
0.6%
0.04 13745
 
0.5%
0.05 10563
 
0.4%
0.06 8575
 
0.3%
0.07 6868
 
0.2%
0.08 5942
 
0.2%
0.09 5303
 
0.2%
Other values (220) 42203
 
1.5%
(Missing) 549458
 
19.3%
ValueCountFrequency (%)
0 2104242
74.0%
0.01 54160
 
1.9%
0.02 26497
 
0.9%
0.03 17786
 
0.6%
0.04 13745
 
0.5%
0.05 10563
 
0.4%
0.06 8575
 
0.3%
0.07 6868
 
0.2%
0.08 5942
 
0.2%
0.09 5303
 
0.2%
ValueCountFrequency (%)
24 5
 
< 0.1%
10.4 2
 
< 0.1%
10.05 1
 
< 0.1%
10.02 3
 
< 0.1%
10.01 1
 
< 0.1%
10 15
 
< 0.1%
9.99 50
< 0.1%
9.98 10
 
< 0.1%
9.97 9
 
< 0.1%
9.96 11
 
< 0.1%

Weather_Condition
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct127
Distinct (%)< 0.1%
Missing70636
Missing (%)2.5%
Memory size21.7 MiB
Fair
1107194 
Mostly Cloudy
363959 
Cloudy
348767 
Partly Cloudy
249939 
Clear
173823 
Other values (122)
531024 

Length

Max length35
Median length30
Mean length7.3815903
Min length3

Characters and Unicode

Total characters20481743
Distinct characters46
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)< 0.1%

Sample

1st rowLight Rain
2nd rowLight Rain
3rd rowOvercast
4th rowOvercast
5th rowLight Rain

Common Values

ValueCountFrequency (%)
Fair 1107194
38.9%
Mostly Cloudy 363959
 
12.8%
Cloudy 348767
 
12.3%
Partly Cloudy 249939
 
8.8%
Clear 173823
 
6.1%
Light Rain 128403
 
4.5%
Overcast 84882
 
3.0%
Scattered Clouds 45132
 
1.6%
Light Snow 43752
 
1.5%
Fog 41226
 
1.4%
Other values (117) 187629
 
6.6%
(Missing) 70636
 
2.5%

Length

2023-02-28T17:09:30.139252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fair 1122389
29.6%
cloudy 979677
25.8%
mostly 370256
 
9.8%
partly 253815
 
6.7%
light 193371
 
5.1%
rain 185128
 
4.9%
clear 173823
 
4.6%
overcast 84882
 
2.2%
snow 53748
 
1.4%
scattered 45132
 
1.2%
Other values (51) 328144
 
8.7%

Most occurring characters

ValueCountFrequency (%)
a 1926734
 
9.4%
l 1835427
 
9.0%
r 1732864
 
8.5%
y 1676635
 
8.2%
i 1603157
 
7.8%
o 1518576
 
7.4%
C 1198634
 
5.9%
F 1168184
 
5.7%
d 1136664
 
5.5%
u 1046822
 
5.1%
Other values (36) 5638046
27.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15675963
76.5%
Uppercase Letter 3735727
 
18.2%
Space Separator 1015659
 
5.0%
Other Punctuation 43608
 
0.2%
Dash Punctuation 10786
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1926734
12.3%
l 1835427
11.7%
r 1732864
11.1%
y 1676635
10.7%
i 1603157
10.2%
o 1518576
9.7%
d 1136664
7.3%
u 1046822
6.7%
t 1035948
6.6%
s 508898
 
3.2%
Other values (15) 1654238
10.6%
Uppercase Letter
ValueCountFrequency (%)
C 1198634
32.1%
F 1168184
31.3%
M 375193
 
10.0%
P 256084
 
6.9%
L 193372
 
5.2%
R 185128
 
5.0%
S 119203
 
3.2%
O 84882
 
2.3%
H 55817
 
1.5%
W 46515
 
1.2%
Other values (8) 52715
 
1.4%
Space Separator
ValueCountFrequency (%)
1015659
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 43608
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10786
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19411690
94.8%
Common 1070053
 
5.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1926734
9.9%
l 1835427
9.5%
r 1732864
 
8.9%
y 1676635
 
8.6%
i 1603157
 
8.3%
o 1518576
 
7.8%
C 1198634
 
6.2%
F 1168184
 
6.0%
d 1136664
 
5.9%
u 1046822
 
5.4%
Other values (33) 4567993
23.5%
Common
ValueCountFrequency (%)
1015659
94.9%
/ 43608
 
4.1%
- 10786
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20481743
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1926734
 
9.4%
l 1835427
 
9.0%
r 1732864
 
8.5%
y 1676635
 
8.2%
i 1603157
 
7.8%
o 1518576
 
7.4%
C 1198634
 
5.9%
F 1168184
 
5.7%
d 1136664
 
5.5%
u 1046822
 
5.1%
Other values (36) 5638046
27.5%

Amenity
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
False
2817352 
True
 
27990
ValueCountFrequency (%)
False 2817352
99.0%
True 27990
 
1.0%
2023-02-28T17:09:30.205636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Bump
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
False
2844321 
True
 
1021
ValueCountFrequency (%)
False 2844321
> 99.9%
True 1021
 
< 0.1%
2023-02-28T17:09:30.257731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Crossing
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
False
2645130 
True
 
200212
ValueCountFrequency (%)
False 2645130
93.0%
True 200212
 
7.0%
2023-02-28T17:09:30.307348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Give_Way
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
False
2838474 
True
 
6868
ValueCountFrequency (%)
False 2838474
99.8%
True 6868
 
0.2%
2023-02-28T17:09:30.355520image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Junction
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
False
2554837 
True
290505 
ValueCountFrequency (%)
False 2554837
89.8%
True 290505
 
10.2%
2023-02-28T17:09:30.404000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

No_Exit
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
False
2841048 
True
 
4294
ValueCountFrequency (%)
False 2841048
99.8%
True 4294
 
0.2%
2023-02-28T17:09:30.453521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Railway
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
False
2822711 
True
 
22631
ValueCountFrequency (%)
False 2822711
99.2%
True 22631
 
0.8%
2023-02-28T17:09:30.501226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Roundabout
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
False
2845219 
True
 
123
ValueCountFrequency (%)
False 2845219
> 99.9%
True 123
 
< 0.1%
2023-02-28T17:09:30.549364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Station
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
False
2777347 
True
 
67995
ValueCountFrequency (%)
False 2777347
97.6%
True 67995
 
2.4%
2023-02-28T17:09:30.597725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Stop
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
False
2794942 
True
 
50400
ValueCountFrequency (%)
False 2794942
98.2%
True 50400
 
1.8%
2023-02-28T17:09:30.648314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Traffic_Calming
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
False
2843630 
True
 
1712
ValueCountFrequency (%)
False 2843630
99.9%
True 1712
 
0.1%
2023-02-28T17:09:30.698412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
False
2580079 
True
265263 
ValueCountFrequency (%)
False 2580079
90.7%
True 265263
 
9.3%
2023-02-28T17:09:30.747090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
False
2845342 
ValueCountFrequency (%)
False 2845342
100.0%
2023-02-28T17:09:30.795125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Sunrise_Sunset
Categorical

Distinct2
Distinct (%)< 0.1%
Missing2867
Missing (%)0.1%
Memory size21.7 MiB
Day
1811935 
Night
1030540 

Length

Max length5
Median length3
Mean length3.7251005
Min length3

Characters and Unicode

Total characters10588505
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNight
2nd rowNight
3rd rowNight
4th rowNight
5th rowDay

Common Values

ValueCountFrequency (%)
Day 1811935
63.7%
Night 1030540
36.2%
(Missing) 2867
 
0.1%

Length

2023-02-28T17:09:30.844442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T17:09:30.903713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
day 1811935
63.7%
night 1030540
36.3%

Most occurring characters

ValueCountFrequency (%)
D 1811935
17.1%
a 1811935
17.1%
y 1811935
17.1%
N 1030540
9.7%
i 1030540
9.7%
g 1030540
9.7%
h 1030540
9.7%
t 1030540
9.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7746030
73.2%
Uppercase Letter 2842475
 
26.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1811935
23.4%
y 1811935
23.4%
i 1030540
13.3%
g 1030540
13.3%
h 1030540
13.3%
t 1030540
13.3%
Uppercase Letter
ValueCountFrequency (%)
D 1811935
63.7%
N 1030540
36.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 10588505
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 1811935
17.1%
a 1811935
17.1%
y 1811935
17.1%
N 1030540
9.7%
i 1030540
9.7%
g 1030540
9.7%
h 1030540
9.7%
t 1030540
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10588505
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 1811935
17.1%
a 1811935
17.1%
y 1811935
17.1%
N 1030540
9.7%
i 1030540
9.7%
g 1030540
9.7%
h 1030540
9.7%
t 1030540
9.7%

Civil_Twilight
Categorical

Distinct2
Distinct (%)< 0.1%
Missing2867
Missing (%)0.1%
Memory size21.7 MiB
Day
1929103 
Night
913372 

Length

Max length5
Median length3
Mean length3.6426597
Min length3

Characters and Unicode

Total characters10354169
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNight
2nd rowNight
3rd rowNight
4th rowNight
5th rowDay

Common Values

ValueCountFrequency (%)
Day 1929103
67.8%
Night 913372
32.1%
(Missing) 2867
 
0.1%

Length

2023-02-28T17:09:30.955357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T17:09:31.014876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
day 1929103
67.9%
night 913372
32.1%

Most occurring characters

ValueCountFrequency (%)
D 1929103
18.6%
a 1929103
18.6%
y 1929103
18.6%
N 913372
8.8%
i 913372
8.8%
g 913372
8.8%
h 913372
8.8%
t 913372
8.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7511694
72.5%
Uppercase Letter 2842475
 
27.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1929103
25.7%
y 1929103
25.7%
i 913372
12.2%
g 913372
12.2%
h 913372
12.2%
t 913372
12.2%
Uppercase Letter
ValueCountFrequency (%)
D 1929103
67.9%
N 913372
32.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 10354169
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 1929103
18.6%
a 1929103
18.6%
y 1929103
18.6%
N 913372
8.8%
i 913372
8.8%
g 913372
8.8%
h 913372
8.8%
t 913372
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10354169
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 1929103
18.6%
a 1929103
18.6%
y 1929103
18.6%
N 913372
8.8%
i 913372
8.8%
g 913372
8.8%
h 913372
8.8%
t 913372
8.8%
Distinct2
Distinct (%)< 0.1%
Missing2867
Missing (%)0.1%
Memory size21.7 MiB
Day
2063472 
Night
779003 

Length

Max length5
Median length3
Mean length3.548116
Min length3

Characters and Unicode

Total characters10085431
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNight
2nd rowNight
3rd rowNight
4th rowDay
5th rowDay

Common Values

ValueCountFrequency (%)
Day 2063472
72.5%
Night 779003
 
27.4%
(Missing) 2867
 
0.1%

Length

2023-02-28T17:09:31.065901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T17:09:31.125293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
day 2063472
72.6%
night 779003
 
27.4%

Most occurring characters

ValueCountFrequency (%)
D 2063472
20.5%
a 2063472
20.5%
y 2063472
20.5%
N 779003
 
7.7%
i 779003
 
7.7%
g 779003
 
7.7%
h 779003
 
7.7%
t 779003
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7242956
71.8%
Uppercase Letter 2842475
 
28.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2063472
28.5%
y 2063472
28.5%
i 779003
 
10.8%
g 779003
 
10.8%
h 779003
 
10.8%
t 779003
 
10.8%
Uppercase Letter
ValueCountFrequency (%)
D 2063472
72.6%
N 779003
 
27.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 10085431
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 2063472
20.5%
a 2063472
20.5%
y 2063472
20.5%
N 779003
 
7.7%
i 779003
 
7.7%
g 779003
 
7.7%
h 779003
 
7.7%
t 779003
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10085431
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 2063472
20.5%
a 2063472
20.5%
y 2063472
20.5%
N 779003
 
7.7%
i 779003
 
7.7%
g 779003
 
7.7%
h 779003
 
7.7%
t 779003
 
7.7%
Distinct2
Distinct (%)< 0.1%
Missing2867
Missing (%)0.1%
Memory size21.7 MiB
Day
2176983 
Night
665492 

Length

Max length5
Median length3
Mean length3.4682483
Min length3

Characters and Unicode

Total characters9858409
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNight
2nd rowNight
3rd rowDay
4th rowDay
5th rowDay

Common Values

ValueCountFrequency (%)
Day 2176983
76.5%
Night 665492
 
23.4%
(Missing) 2867
 
0.1%

Length

2023-02-28T17:09:31.178224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T17:09:31.240729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
day 2176983
76.6%
night 665492
 
23.4%

Most occurring characters

ValueCountFrequency (%)
D 2176983
22.1%
a 2176983
22.1%
y 2176983
22.1%
N 665492
 
6.8%
i 665492
 
6.8%
g 665492
 
6.8%
h 665492
 
6.8%
t 665492
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7015934
71.2%
Uppercase Letter 2842475
28.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2176983
31.0%
y 2176983
31.0%
i 665492
 
9.5%
g 665492
 
9.5%
h 665492
 
9.5%
t 665492
 
9.5%
Uppercase Letter
ValueCountFrequency (%)
D 2176983
76.6%
N 665492
 
23.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 9858409
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 2176983
22.1%
a 2176983
22.1%
y 2176983
22.1%
N 665492
 
6.8%
i 665492
 
6.8%
g 665492
 
6.8%
h 665492
 
6.8%
t 665492
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9858409
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 2176983
22.1%
a 2176983
22.1%
y 2176983
22.1%
N 665492
 
6.8%
i 665492
 
6.8%
g 665492
 
6.8%
h 665492
 
6.8%
t 665492
 
6.8%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.7 MiB
Accident
1889425 
Incident
856251 
Cautionary events
 
99666

Length

Max length17
Median length8
Mean length8.31525
Min length8

Characters and Unicode

Total characters23659730
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAccident
2nd rowAccident
3rd rowAccident
4th rowAccident
5th rowAccident

Common Values

ValueCountFrequency (%)
Accident 1889425
66.4%
Incident 856251
30.1%
Cautionary events 99666
 
3.5%

Length

2023-02-28T17:09:31.290463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T17:09:31.346498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
accident 1889425
64.2%
incident 856251
29.1%
cautionary 99666
 
3.4%
events 99666
 
3.4%

Most occurring characters

ValueCountFrequency (%)
c 4635101
19.6%
n 3801259
16.1%
e 2945008
12.4%
t 2945008
12.4%
i 2845342
12.0%
d 2745676
11.6%
A 1889425
8.0%
I 856251
 
3.6%
a 199332
 
0.8%
y 99666
 
0.4%
Other values (7) 697662
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 20714722
87.6%
Uppercase Letter 2845342
 
12.0%
Space Separator 99666
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 4635101
22.4%
n 3801259
18.4%
e 2945008
14.2%
t 2945008
14.2%
i 2845342
13.7%
d 2745676
13.3%
a 199332
 
1.0%
y 99666
 
0.5%
v 99666
 
0.5%
r 99666
 
0.5%
Other values (3) 298998
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
A 1889425
66.4%
I 856251
30.1%
C 99666
 
3.5%
Space Separator
ValueCountFrequency (%)
99666
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23560064
99.6%
Common 99666
 
0.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 4635101
19.7%
n 3801259
16.1%
e 2945008
12.5%
t 2945008
12.5%
i 2845342
12.1%
d 2745676
11.7%
A 1889425
8.0%
I 856251
 
3.6%
a 199332
 
0.8%
y 99666
 
0.4%
Other values (6) 597996
 
2.5%
Common
ValueCountFrequency (%)
99666
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23659730
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 4635101
19.6%
n 3801259
16.1%
e 2945008
12.4%
t 2945008
12.4%
i 2845342
12.0%
d 2745676
11.6%
A 1889425
8.0%
I 856251
 
3.6%
a 199332
 
0.8%
y 99666
 
0.4%
Other values (7) 697662
 
2.9%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.7 MiB
Accident
1889402 
Incident
856251 
Cautionary events
 
99491
Rollover
 
198

Length

Max length17
Median length8
Mean length8.3146964
Min length8

Characters and Unicode

Total characters23658155
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAccident
2nd rowAccident
3rd rowAccident
4th rowAccident
5th rowAccident

Common Values

ValueCountFrequency (%)
Accident 1889402
66.4%
Incident 856251
30.1%
Cautionary events 99491
 
3.5%
Rollover 198
 
< 0.1%

Length

2023-02-28T17:09:31.397836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T17:09:31.455101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
accident 1889402
64.2%
incident 856251
29.1%
cautionary 99491
 
3.4%
events 99491
 
3.4%
rollover 198
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
c 4635055
19.6%
n 3800886
16.1%
e 2944833
12.4%
t 2944635
12.4%
i 2845144
12.0%
d 2745653
11.6%
A 1889402
8.0%
I 856251
 
3.6%
a 198982
 
0.8%
o 99887
 
0.4%
Other values (9) 697427
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 20713322
87.6%
Uppercase Letter 2845342
 
12.0%
Space Separator 99491
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 4635055
22.4%
n 3800886
18.3%
e 2944833
14.2%
t 2944635
14.2%
i 2845144
13.7%
d 2745653
13.3%
a 198982
 
1.0%
o 99887
 
0.5%
r 99689
 
0.5%
v 99689
 
0.5%
Other values (4) 298869
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
A 1889402
66.4%
I 856251
30.1%
C 99491
 
3.5%
R 198
 
< 0.1%
Space Separator
ValueCountFrequency (%)
99491
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23558664
99.6%
Common 99491
 
0.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 4635055
19.7%
n 3800886
16.1%
e 2944833
12.5%
t 2944635
12.5%
i 2845144
12.1%
d 2745653
11.7%
A 1889402
8.0%
I 856251
 
3.6%
a 198982
 
0.8%
o 99887
 
0.4%
Other values (8) 597936
 
2.5%
Common
ValueCountFrequency (%)
99491
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23658155
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 4635055
19.6%
n 3800886
16.1%
e 2944833
12.4%
t 2944635
12.4%
i 2845144
12.0%
d 2745653
11.6%
A 1889402
8.0%
I 856251
 
3.6%
a 198982
 
0.8%
o 99887
 
0.4%
Other values (9) 697427
 
2.9%

date
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size21.7 MiB

Interactions

2023-02-28T17:08:22.194067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:22.804602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:28.530741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:33.758555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:39.082446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:44.409448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:49.095485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:52.086111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:57.512855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:02.183967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:07.523636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:12.356009image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:17.276086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:22.507463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:23.247355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:28.926021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:34.168175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:39.489544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:44.783644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:49.322605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:52.530994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:57.884125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:02.592787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:07.922952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:12.732416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:17.637288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:22.816311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:23.664609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:29.309449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:34.560493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:39.877843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:45.143629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:49.536934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:52.942361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:58.252300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:03.007974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:08.317009image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:13.108104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:17.990185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:23.131278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:24.072723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:29.692743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:34.955785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:40.259885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:45.485890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:49.745974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:53.417315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:58.628501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:03.421477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:08.688086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:13.477256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:18.344286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:23.464526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:24.488819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:30.072105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:35.359667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:40.635651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:45.829641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:49.954574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:53.826228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:58.987104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:03.837022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:09.063842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:13.852189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:18.700191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:23.696078image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:25.002045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:30.320245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:35.619059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:41.002896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:46.082512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:50.161460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:54.090724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:59.207469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:04.112260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:09.320043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:14.112725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:18.954620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:24.022289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:25.499723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:30.818770image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:36.116980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:41.488545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:46.556403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:50.367446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:54.576761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:59.562251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:04.604347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:09.761743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:14.575737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:19.611259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:24.339338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:25.887684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:31.190023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:36.494728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:41.872440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:46.893476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:50.590775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:54.971982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:59.924182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:04.988743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:10.102133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:14.921923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:19.940827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:24.661292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:26.313075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:31.602358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:36.915605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:42.287356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:47.272438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:50.780167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:55.400780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:00.302691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:05.417668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:10.472367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:15.301148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:20.297121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:24.981379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:26.813128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:32.120857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:37.432356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:42.799228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:47.763262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:50.976957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:55.825480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:00.672219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:05.835741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:10.911385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:15.746576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:20.759674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:25.290226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:27.254314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:32.541581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:37.851147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:43.237753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:48.153199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:51.170510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:56.238972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:01.037518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:06.244926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:11.294375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:16.190290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:21.129688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:25.606265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:27.681641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:32.936894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:38.250653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:43.632661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:48.519043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:51.367520image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:56.640789image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:01.407170image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:06.644113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:11.650939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:16.579281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:21.548582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:25.932736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:28.039311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:33.274097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:38.599634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:43.973215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:48.843097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:51.587460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:07:56.988118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:01.757072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:06.989564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:11.980977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:16.914654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T17:08:21.876492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-02-28T17:09:31.535525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Start_LatStart_LngEnd_LatEnd_LngDistance(mi)NumberTemperature(F)Wind_Chill(F)Humidity(%)Pressure(in)Visibility(mi)Wind_Speed(mph)Precipitation(in)SeveritySideStateTimezoneWind_DirectionAmenityBumpCrossingGive_WayJunctionNo_ExitRailwayRoundaboutStationStopTraffic_CalmingTraffic_SignalSunrise_SunsetCivil_TwilightNautical_TwilightAstronomical_Twilighttraffic_event_typetraffic_event_type_ext
Start_Lat1.000-0.0591.000-0.0590.077-0.057-0.460-0.4900.020-0.240-0.0910.0290.0640.0960.1000.7380.4560.1180.0410.0120.1450.0290.0840.0310.0200.0070.1710.0420.0130.0760.0510.0460.0390.0410.1460.120
Start_Lng-0.0591.000-0.0591.0000.133-0.2270.0630.0520.1010.1460.0670.0860.0220.1490.0870.8530.9470.1150.0320.0290.1360.0350.0690.0250.0260.0030.1040.0650.0220.0980.0470.0440.0370.0340.1550.127
End_Lat1.000-0.0591.000-0.0590.077-0.057-0.460-0.4900.020-0.240-0.0910.0290.0640.0950.1010.7360.4540.1180.0410.0120.1450.0290.0830.0310.0200.0070.1710.0420.0130.0760.0510.0450.0390.0400.1470.120
End_Lng-0.0591.000-0.0591.0000.133-0.2270.0630.0520.1010.1460.0670.0860.0220.1490.0870.8530.9470.1150.0320.0290.1360.0350.0700.0250.0260.0030.1040.0650.0220.0980.0470.0440.0370.0340.1550.127
Distance(mi)0.0770.1330.0770.1331.0000.097-0.036-0.0460.048-0.008-0.0290.0120.0250.0260.0040.0330.0170.0040.0020.0000.0070.0000.0010.0000.0010.0000.0050.0010.0000.0080.0060.0060.0050.0060.0040.003
Number-0.057-0.227-0.057-0.2270.0971.0000.0350.0430.0030.041-0.0070.0120.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Temperature(F)-0.4600.063-0.4600.063-0.0360.0351.0000.999-0.3640.0580.2200.140-0.1130.0360.0410.1880.1310.0750.0130.0040.0600.0050.0200.0130.0090.0020.0530.0080.0050.0410.3340.3150.2940.2760.0950.078
Wind_Chill(F)-0.4900.052-0.4900.052-0.0460.0430.9991.000-0.3550.0610.2210.108-0.1180.0610.0580.2070.1560.1100.0170.0060.0700.0070.0440.0140.0110.0020.0630.0130.0070.0460.3360.3170.2960.2790.0890.073
Humidity(%)0.0200.1010.0200.1010.0480.003-0.364-0.3551.0000.089-0.438-0.2030.3470.0610.0220.1490.1990.0900.0150.0100.0390.0050.0110.0100.0050.0000.0430.0220.0070.0390.3120.2950.2790.2610.0690.057
Pressure(in)-0.2400.146-0.2400.146-0.0080.0410.0580.0610.0891.0000.072-0.005-0.0170.0400.0180.2270.2110.0830.0170.0030.0280.0040.0410.0010.0180.0000.0550.0100.0000.0140.0320.0320.0310.0310.0850.069
Visibility(mi)-0.0910.067-0.0910.067-0.029-0.0070.2200.221-0.4380.0721.0000.080-0.4080.0310.0110.0920.0740.0150.0030.0000.0040.0100.0080.0070.0040.0000.0070.0000.0000.0040.0170.0180.0180.0170.0180.014
Wind_Speed(mph)0.0290.0860.0290.0860.0120.0120.1400.108-0.203-0.0050.0801.0000.0810.0020.0000.0000.0000.0020.0000.0000.0010.0000.0010.0000.0000.0000.0000.0000.0000.0010.0020.0020.0020.0020.0000.000
Precipitation(in)0.0640.0220.0640.0220.0250.006-0.113-0.1180.347-0.017-0.4080.0811.0000.0040.0020.0140.0050.0340.0050.0000.0030.0000.0060.0000.0010.0000.0000.0000.0000.0050.0020.0020.0030.0030.0040.003
Severity0.0960.1490.0950.1490.0260.0000.0360.0610.0610.0400.0310.0020.0041.0000.0440.2320.1120.1410.0130.0040.1000.0120.0790.0120.0140.0010.0190.0180.0030.1220.0520.0530.0520.0510.1460.120
Side0.1000.0870.1010.0870.0040.0000.0410.0580.0220.0180.0110.0000.0020.0441.0000.1160.0570.0620.0510.0060.1030.0000.1440.0110.0160.0030.0810.0530.0050.0920.0100.0080.0060.0060.0490.049
State0.7380.8530.7360.8530.0330.0000.1880.2070.1490.2270.0920.0000.0140.2320.1161.0000.9700.0960.0720.0390.2050.0570.1320.0410.0560.0080.1570.1060.0330.1640.0780.0760.0710.0690.2700.221
Timezone0.4560.9470.4540.9470.0170.0000.1310.1560.1990.2110.0740.0000.0050.1120.0570.9701.0000.1390.0150.0210.0980.0220.0470.0200.0230.0010.0560.0410.0130.0750.0320.0300.0230.0200.1300.107
Wind_Direction0.1180.1150.1180.1150.0040.0000.0750.1100.0900.0830.0150.0020.0340.1410.0620.0960.1391.0000.0170.0050.0530.0040.1020.0090.0050.0020.0500.0230.0060.0310.2210.2160.2070.1960.1710.140
Amenity0.0410.0320.0410.0320.0020.0000.0130.0170.0150.0170.0030.0000.0050.0130.0510.0720.0150.0171.0000.0050.1180.0030.0260.0130.0340.0000.1240.0270.0110.0910.0060.0050.0030.0030.0060.006
Bump0.0120.0290.0120.0290.0000.0000.0040.0060.0100.0030.0000.0000.0000.0040.0060.0390.0210.0050.0051.0000.0130.0000.0020.0020.0060.0000.0060.0190.7720.0040.0020.0020.0020.0020.0080.008
Crossing0.1450.1360.1450.1360.0070.0000.0600.0700.0390.0280.0040.0010.0030.1000.1030.2050.0980.0530.1180.0131.0000.0530.0800.0420.2070.0000.1450.0870.0260.4220.0220.0190.0160.0130.0280.028
Give_Way0.0290.0350.0290.0350.0000.0000.0050.0070.0050.0040.0100.0000.0000.0120.0000.0570.0220.0040.0030.0000.0531.0000.0070.0040.0040.0020.0020.0480.0000.0570.0000.0000.0000.0000.0070.007
Junction0.0840.0690.0830.0700.0010.0000.0200.0440.0110.0410.0080.0010.0060.0790.1440.1320.0470.1020.0260.0020.0800.0071.0000.0040.0100.0120.0440.0350.0020.0960.0050.0060.0080.0080.0850.085
No_Exit0.0310.0250.0310.0250.0000.0000.0130.0140.0100.0010.0070.0000.0000.0120.0110.0410.0200.0090.0130.0020.0420.0040.0041.0000.0030.0000.0150.0120.0010.0230.0010.0000.0000.0000.0020.002
Railway0.0200.0260.0200.0260.0010.0000.0090.0110.0050.0180.0040.0000.0010.0140.0160.0560.0230.0050.0340.0060.2070.0040.0100.0031.0000.0000.1090.0080.0050.0540.0010.0010.0010.0010.0020.002
Roundabout0.0070.0030.0070.0030.0000.0000.0020.0020.0000.0000.0000.0000.0000.0010.0030.0080.0010.0020.0000.0000.0000.0020.0120.0000.0001.0000.0000.0060.0010.0020.0000.0000.0000.0010.0060.006
Station0.1710.1040.1710.1040.0050.0000.0530.0630.0430.0550.0070.0000.0000.0190.0810.1570.0560.0500.1240.0060.1450.0020.0440.0150.1090.0001.0000.0270.0100.1130.0200.0190.0160.0160.0230.023
Stop0.0420.0650.0420.0650.0010.0000.0080.0130.0220.0100.0000.0000.0000.0180.0530.1060.0410.0230.0270.0190.0870.0480.0350.0120.0080.0060.0271.0000.0170.0280.0100.0110.0120.0120.0450.045
Traffic_Calming0.0130.0220.0130.0220.0000.0000.0050.0070.0070.0000.0000.0000.0000.0030.0050.0330.0130.0060.0110.7720.0260.0000.0020.0010.0050.0010.0100.0171.0000.0090.0010.0010.0020.0010.0070.007
Traffic_Signal0.0760.0980.0760.0980.0080.0000.0410.0460.0390.0140.0040.0010.0050.1220.0920.1640.0750.0310.0910.0040.4220.0570.0960.0230.0540.0020.1130.0280.0091.0000.0180.0150.0110.0080.0390.039
Sunrise_Sunset0.0510.0470.0510.0470.0060.0000.3340.3360.3120.0320.0170.0020.0020.0520.0100.0780.0320.2210.0060.0020.0220.0000.0050.0010.0010.0000.0200.0100.0010.0181.0000.9120.8150.7300.1860.186
Civil_Twilight0.0460.0440.0450.0440.0060.0000.3150.3170.2950.0320.0180.0020.0020.0530.0080.0760.0300.2160.0050.0020.0190.0000.0060.0000.0010.0000.0190.0110.0010.0150.9121.0000.8930.8000.1970.197
Nautical_Twilight0.0390.0370.0390.0370.0050.0000.2940.2960.2790.0310.0180.0020.0030.0520.0060.0710.0230.2070.0030.0020.0160.0000.0080.0000.0010.0000.0160.0120.0020.0110.8150.8931.0000.8960.2100.210
Astronomical_Twilight0.0410.0340.0400.0340.0060.0000.2760.2790.2610.0310.0170.0020.0030.0510.0060.0690.0200.1960.0030.0020.0130.0000.0080.0000.0010.0010.0160.0120.0010.0080.7300.8000.8961.0000.2200.220
traffic_event_type0.1460.1550.1470.1550.0040.0000.0950.0890.0690.0850.0180.0000.0040.1460.0490.2700.1300.1710.0060.0080.0280.0070.0850.0020.0020.0060.0230.0450.0070.0390.1860.1970.2100.2201.0001.000
traffic_event_type_ext0.1200.1270.1200.1270.0030.0000.0780.0730.0570.0690.0140.0000.0030.1200.0490.2210.1070.1400.0060.0080.0280.0070.0850.0020.0020.0060.0230.0450.0070.0390.1860.1970.2100.2201.0001.000

Missing values

2023-02-28T17:08:32.731571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-28T17:08:47.754120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-02-28T17:09:16.660511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IDSeverityStart_TimeEnd_TimeStart_LatStart_LngEnd_LatEnd_LngDistance(mi)DescriptionNumberStreetSideCityCountyStateZipcodeCountryTimezoneAirport_CodeWeather_TimestampTemperature(F)Wind_Chill(F)Humidity(%)Pressure(in)Visibility(mi)Wind_DirectionWind_Speed(mph)Precipitation(in)Weather_ConditionAmenityBumpCrossingGive_WayJunctionNo_ExitRailwayRoundaboutStationStopTraffic_CalmingTraffic_SignalTurning_LoopSunrise_SunsetCivil_TwilightNautical_TwilightAstronomical_Twilighttraffic_event_typetraffic_event_type_extdate
0A-132016-02-08 00:37:082016-02-08 06:37:0840.108910-83.09286040.112060-83.0318703.230Between Sawmill Rd/Exit 20 and OH-315/Olentangy Riv Rd/Exit 22 - Accident.NaNOuterbelt ERDublinFranklinOH43017USUS/EasternKOSU2016-02-08 00:53:0042.136.158.029.7610.0SW10.40.00Light RainFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseNightNightNightNightAccidentAccident2016-02-08
1A-222016-02-08 05:56:202016-02-08 11:56:2039.865420-84.06280039.865010-84.0487300.747At OH-4/OH-235/Exit 41 - Accident.NaNI-70 ERDaytonMontgomeryOH45424USUS/EasternKFFO2016-02-08 05:58:0036.9NaN91.029.6810.0CalmNaN0.02Light RainFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseNightNightNightNightAccidentAccident2016-02-08
2A-322016-02-08 06:15:392016-02-08 12:15:3939.102660-84.52468039.102090-84.5239600.055At I-71/US-50/Exit 1 - Accident.NaNI-75 SRCincinnatiHamiltonOH45203USUS/EasternKLUK2016-02-08 05:53:0036.0NaN97.029.7010.0CalmNaN0.02OvercastFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseNightNightNightDayAccidentAccident2016-02-08
3A-422016-02-08 06:51:452016-02-08 12:51:4541.062130-81.53784041.062170-81.5354700.123At Dart Ave/Exit 21 - Accident.NaNI-77 NRAkronSummitOH44311USUS/EasternKAKR2016-02-08 06:54:0039.0NaN55.029.6510.0CalmNaNNaNOvercastFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseNightNightDayDayAccidentAccident2016-02-08
4A-532016-02-08 07:53:432016-02-08 13:53:4339.172393-84.49279239.170476-84.5017980.500At Mitchell Ave/Exit 6 - Accident.NaNI-75 SRCincinnatiHamiltonOH45217USUS/EasternKLUK2016-02-08 07:53:0037.029.893.029.6910.0WSW10.40.01Light RainFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDayAccidentAccident2016-02-08
5A-622016-02-08 08:16:572016-02-08 14:16:5739.063240-84.03243039.067310-84.0585101.427At Dela Palma Rd - Accident.NaNState Route 32RWilliamsburgClermontOH45176USUS/EasternKI692016-02-08 08:16:0035.629.2100.029.6610.0WSW8.1NaNOvercastFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseDayDayDayDayAccidentAccident2016-02-08
6A-722016-02-08 08:15:412016-02-08 14:15:4139.775650-84.18603039.772750-84.1880500.227At OH-4/Exit 54 - Accident.NaNI-75 SRDaytonMontgomeryOH45404USUS/EasternKFFO2016-02-08 08:18:0033.8NaN100.029.633.0SW2.3NaNMostly CloudyFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDayAccidentAccident2016-02-08
7A-822016-02-08 11:51:462016-02-08 17:51:4641.375310-81.82017041.367860-81.8217400.521At Bagley Rd/Exit 235 - Accident.NaNI-71 SRClevelandCuyahogaOH44130USUS/EasternKCLE2016-02-08 11:51:0033.130.092.029.630.5SW3.50.08SnowFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDayAccidentAccident2016-02-08
8A-922016-02-08 14:19:572016-02-08 20:19:5740.702247-84.07588740.699110-84.0842930.491At OH-65/Exit 122 - Accident.NaNE Hanthorn RdRLimaAllenOH45806USUS/EasternKAOH2016-02-08 13:53:0039.031.870.029.5910.0WNW11.5NaNOvercastFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDayAccidentAccident2016-02-08
9A-1022016-02-08 15:16:432016-02-08 21:16:4340.109310-82.96849040.110780-82.9840000.826At I-71/Exit 26 - Accident.NaNOuterbelt WRWestervilleFranklinOH43081USUS/EasternKCMH2016-02-08 15:12:0032.028.7100.029.590.5West3.50.05SnowFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDayAccidentAccident2016-02-08
IDSeverityStart_TimeEnd_TimeStart_LatStart_LngEnd_LatEnd_LngDistance(mi)DescriptionNumberStreetSideCityCountyStateZipcodeCountryTimezoneAirport_CodeWeather_TimestampTemperature(F)Wind_Chill(F)Humidity(%)Pressure(in)Visibility(mi)Wind_DirectionWind_Speed(mph)Precipitation(in)Weather_ConditionAmenityBumpCrossingGive_WayJunctionNo_ExitRailwayRoundaboutStationStopTraffic_CalmingTraffic_SignalTurning_LoopSunrise_SunsetCivil_TwilightNautical_TwilightAstronomical_Twilighttraffic_event_typetraffic_event_type_extdate
2845332A-284533322019-08-23 17:42:272019-08-23 18:11:1034.064460-118.00388034.065330-117.9971500.390At I-605 - Accident.NaNI-10 ERBaldwin ParkLos AngelesCA91706USUS/PacificKEMT2019-08-23 17:53:0078.078.052.029.6910.0VAR6.00.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDayAccidentAccident2019-08-23
2845333A-284533422019-08-23 17:40:122019-08-23 18:08:3533.943599-117.07788033.943599-117.0778800.000At Jack Rabbit Trl - Accident.NaNCA-60 ERMoreno ValleyRiversideCA92555USUS/PacificKRIV2019-08-23 17:58:0088.088.032.028.2010.0WNW10.00.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDayAccidentAccident2019-08-23
2845334A-284533522019-08-23 17:40:122019-08-23 18:08:3534.261030-119.22800034.262390-119.2308700.189At Telephone Rd/Exit 65 - Accident.NaNEl Camino Real NRVenturaVenturaCA93003USUS/PacificKOXR2019-08-23 17:51:0073.073.068.029.7610.0W9.00.0FairFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDayAccidentAccident2019-08-23
2845335A-284533622019-08-23 17:43:562019-08-23 18:12:2733.741700-117.83709033.739170-117.8300100.443At CA-55 - Accident.NaNSanta Ana Fwy SRTustinOrangeCA92780USUS/PacificKSNA2019-08-23 17:53:0075.075.060.029.7410.0SSW9.00.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDayAccidentAccident2019-08-23
2845336A-284533722019-08-23 18:30:232019-08-23 18:58:5434.239104-118.41617634.239104-118.4161760.000At Osborne St/Exit 154 - Accident.NaNGolden State Fwy NRPacoimaLos AngelesCA91331USUS/PacificKWHP2019-08-23 18:50:0081.081.048.028.7810.0ESE6.0NaNFairFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDayAccidentAccident2019-08-23
2845337A-284533822019-08-23 18:03:252019-08-23 18:32:0134.002480-117.37936033.998880-117.3709400.543At Market St - Accident.NaNPomona Fwy ERRiversideRiversideCA92501USUS/PacificKRAL2019-08-23 17:53:0086.086.040.028.9210.0W13.00.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDayAccidentAccident2019-08-23
2845338A-284533922019-08-23 19:11:302019-08-23 19:38:2332.766960-117.14806032.765550-117.1536300.338At Camino Del Rio/Mission Center Rd - Accident.NaNI-8 WRSan DiegoSan DiegoCA92108USUS/PacificKMYF2019-08-23 18:53:0070.070.073.029.3910.0SW6.00.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDayAccidentAccident2019-08-23
2845339A-284534022019-08-23 19:00:212019-08-23 19:28:4933.775450-117.84779033.777400-117.8572700.561At Glassell St/Grand Ave - Accident. in the right lane.NaNGarden Grove FwyROrangeOrangeCA92866USUS/PacificKSNA2019-08-23 18:53:0073.073.064.029.7410.0SSW10.00.0Partly CloudyFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDayAccidentAccident2019-08-23
2845340A-284534122019-08-23 19:00:212019-08-23 19:29:4233.992460-118.40302033.983110-118.3956500.772At CA-90/Marina Fwy/Jefferson Blvd - Accident.NaNSan Diego Fwy SRCulver CityLos AngelesCA90230USUS/PacificKSMO2019-08-23 18:51:0071.071.081.029.6210.0SW8.00.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDayAccidentAccident2019-08-23
2845341A-284534222019-08-23 18:52:062019-08-23 19:21:3134.133930-117.23092034.137360-117.2393400.537At Highland Ave/Arden Ave - Accident.NaNCA-210 WRHighlandSan BernardinoCA92346USUS/PacificKSBD2019-08-23 20:50:0079.079.047.028.637.0SW7.00.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseDayDayDayDayAccidentAccident2019-08-23